Journal of Information System Research (JOSH)

Comparison of Nazief–Adriani and Porter Stemmer in Determining Javanese Root Words

Anggi Ayu Maharani, Fadhli Almu’iini Ahda — Sun, 05 Jul 2026 11:16:59 +0700

Stemming is an important step in text processing to convert inflected words into their root word. In Javanese, the stemming process is more challenging due to its complex morphological characteristics, including prefixes, infixes, suffixes, and combinations of affixes that are often accompanied by phonological changes. This study aims to compare the performance of the Nazief–Adriani and Porter Stemmer algorithms in Javanese stemming by including infix processing as part of the stemming stage. The dataset used consists of 603 Javanese affixed words covering various types of affixes. The evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics, as well as an analysis of over-stemming and under-stemming errors. The results show that the Nazief–Adriani algorithm performs better with an accuracy of 90.05%, compared to the Porter Stemmer, which achieved 76.12%. This advantage is influenced by the validation of the dictionary at each stage of affix cutting, so that the stemming results are more controlled. The application of infix processing has also been proven to contribute to improving the accuracy of stemming results. This study is expected to be a reference in the development of natural language processing systems for Javanese and encourage further research related to the refinement of morphological rules.

Segmentasi Citra Wayang Kulit Pandawa Berkompleksitas Visual Tinggi Menggunakan Model U-Net Berbasis Convolutional Neural Network

Krisna Refiansyah, Mutaqin Akbar — Sun, 05 Jul 2026 11:21:06 +0700

Shadow puppetry (wayang kulit) is one of Indonesia's cultural heritages with significant historical and artistic value. The complexity of digital image backgrounds in wayang kulit poses a major challenge in automatic segmentation, particularly due to lighting variations, intricate carving (tatahan) details, and the limitations of conventional methods in handling high visual variability. This study aims to implement a U-Net architecture based on Convolutional Neural Network (CNN) for segmenting images of Pandawa shadow puppet characters encompassing five main characters: Puntadewa, Janaka, Werkudara, Nakula, and Sadewa. The dataset consists of 1,500 independently collected shadow puppet images with ground truth masks divided into 1,093 training, 157 validation, and 250 test data. The U-Net model was trained using the Adam optimizer with an initial learning rate of 1×10⁻⁴, combined Binary Cross-Entropy and Dice Loss function, and 128×128 pixel input size. Early stopping and automatic learning rate adjustment via ReduceLROnPlateau were applied to optimize training and prevent overfitting throughout the learning process. The model achieved Accuracy 95.8%, AUC 98.6%, Dice Coefficient 91.9%, IoU 86.9%, Precision 91.5%, and Recall 95.0% on 250 test data. Previous studies on wayang kulit have been limited to image classification, while U-Net applications have been predominantly found in medical and satellite domains, making this study a novel contribution that addresses an existing research gap and supports the digitalization of Indonesian cultural heritage. The contribution of this study is to provide the first deep learning-based image segmentation model specifically designed to automatically separate Pandawa wayang kulit silhouettes from their backgrounds, demonstrating the effectiveness of U-Net architecture on cultural heritage objects with high visual complexity, and establishing a segmentation performance baseline for the Indonesian visual cultural heritage domain that can serve as a reference for future wayang kulit digitalization system development.

Analisis Sentimen Ulasan Berbahasa Inggris Apex Legends di Steam Menggunakan TF-IDF N-Gram dan Multinomial Naive Bayes

M. Akbar Zidane, Yuli Praptomo Pamungkas Hari Sungkowo — Sun, 05 Jul 2026 11:23:44 +0700

The number of users of the online game Apex Legends continues to increase along with the always active community, which also leads to an increase in the number of user reviews. In this condition, conducting manual review analysis becomes ineffective, especially due to the numerous reviews written in informal English, containing negation words, and also showing an imbalanced sentiment class distribution. In this study, the aim is to classify reviews from Apex Legends users on the Steam platform into positive and negative sentiments using the Multinomial Naive Bayes algorithm with TF-IDF weighting based on N-Gram features with a combination of Unigram and Bigram. The dataset was obtained through web scraping from the Steam platform with a total of 9,000 reviews, followed by preprocessing which resulted in 8,981 valid reviews. However, the data still showed class imbalance. The random undersampling process was then applied to obtain 5,512 balanced data points. The test results show that the model can achieve an accuracy of 0.8132 or 81.32%. For the negative class, the model obtained a precision of 0.79, recall of 0.85, and f1-score of 0.82, while the positive class obtained a precision of 0.83, recall of 0.78, and f1-score of 0.81. The trained model is also applied to a Streamlit based dashboard to support the visualization and prediction of new review sentiments. The contributions of this study are the application of combined N-Gram features (unigram and bigram) to Multinomial Naive Bayes for handling negation context and informal language, the use of random undersampling to address class imbalance, and the deployment of the trained model into a Streamlit-based dashboard that enables direct visualization and sentiment prediction of new reviews.

Analisis Kepuasan Pengguna Sistem E-commerce Penjualan Pakaian sebagai Media Pendukung Pembelajaran Menggunakan Metode EUCS

Sun, 05 Jul 2026 11:26:51 +0700

The development of digital technology in education has encouraged the use of e-commerce systems as learning support media, making user satisfaction evaluation essential to ensure system effectiveness. This study aims to measure the level of user satisfaction with an e-commerce system utilized in digital learning activities. The research employed a quantitative approach using the End-User Computing Satisfaction (EUCS) method, which consists of five dimensions: content, accuracy, format, ease of use, and timeliness. Data were collected through questionnaires distributed to 61 respondents and analyzed using validity testing, reliability testing, mean analysis, and correlation analysis. The results indicate that all questionnaire items are valid and reliable, while all EUCS dimensions obtained mean scores above 4.000, indicating a high level of user satisfaction. The highest mean values were found in the accuracy and ease of use dimensions (4.268), while correlation analysis revealed that the strongest relationship occurred between the content and format dimensions with a correlation coefficient of 0.726. This study contributes by providing empirical evidence regarding user satisfaction with the use of e-commerce systems as learning media and by identifying the relationships among EUCS dimensions that influence user perceptions. In conclusion, the e-commerce system has successfully met user needs and effectively supported digital learning activities.

Analisis Deskriptif Komparatif Pemanfaatan ChatGPT, Kualitas Pemahaman, dan Efisiensi Tugas Berdasarkan Status Kerja Mahasiswa

Jonathan Wijaya, R Widya Henisaputri — Sun, 05 Jul 2026 11:29:56 +0700

The use of ChatGPT in academic activities may be perceived differently in relation to students’ understanding and task efficiency. Differences in academic demands between working and non-working students may also lead to different patterns of use. This study aimed to describe ChatGPT Utilization, Quality of Understanding, and Task Efficiency, compare the three constructs according to student employment status, and compare Quality of Understanding and Task Efficiency within the same respondents. A quantitative descriptive-comparative design was employed. Data were collected from 101 Universitas Universal students selected through purposive sampling using a four-point Likert-scale questionnaire. Instrument evaluation resulted in 26 final items across three constructs, with Cronbach’s Alpha values ranging from 0.787 to 0.913. The Mann–Whitney U test indicated no significant differences between working and non-working students in ChatGPT Utilization (p=0.923), Quality of Understanding (p=0.244), or Task Efficiency (p=0.079). The Wilcoxon Signed-Rank Test showed that the mean item score for Task Efficiency was higher than that for Quality of Understanding, at 3.260 and 3.000, respectively (Z=−5.779; p<0.001; r=0.593). Based on respondents’ perceptions, ChatGPT use was more prominent in supporting practical and efficient task completion than in the quality of understanding, while student employment status did not produce meaningful differences across the three constructs. This study contributes empirical evidence by distinguishing utilization, understanding, and efficiency as separate aspects and provides a practical basis for higher education institutions to guide ChatGPT use while maintaining information verification and students’ understanding processes.

Analisis Hubungan Preferensi Genre Musik dan Kesehatan Mental pada Dataset MXMH

jafar Jaya Priambudhi, Imam Suharjo — Sun, 05 Jul 2026 11:33:30 +0700

Mental health is a critical issue that is influenced by various factors, including music-listening habits. In the field of music psychology, music genre preferences are known to be associated with emotional regulation and an individual’s psychological state. This study aims to analyze the relationship between music genre preferences and mental health using a data-driven approach. The dataset used is the Music & Mental Health Survey (MXMH), which consists of 737 respondents with variables including music genre preferences, duration of music listening, and mental health indicators such as anxiety, depression, insomnia, and obsessive-compulsive disorder (OCD). The research stages included data preprocessing, exploratory data analysis (EDA), determining the number of clusters using the Elbow Method and Silhouette Score, clustering using the K-Means algorithm, analyzing the relationship between music genre and mental health, and classifying the clustering results using Random Forest. The results showed that respondents could be grouped into three clusters with distinct mental health characteristics. A Silhouette Score of 0.2246 indicates that the quality of cluster separation is still relatively low, making the segmentation results more exploratory in nature. Correlation analysis revealed a positive relationship between the anxiety and depression variables, as well as differences in music genre preference patterns among groups with different mental health conditions. The feature importance results show that the music genre preference variable contributes to distinguishing the characteristics of each cluster. The contribution of this study is to provide an empirical overview of the relationship between music genre preferences and mental health conditions based on the MXMH dataset through a machine learning-based segmentation and classification approach. The findings of this study suggest that music preferences have the potential to be used as an indicator for understanding patterns of an individual’s psychological state, although further validation and methodological development are needed to achieve a more robust segmentation.

Klasifikasi Mutu Tomat dan Potensi Umur Simpan Berdasarkan Fitur Warna-Tekstur Menggunakan Random Forest

Intan Noviyanti, Esti Wijayanti, Evanita Evanita — Sun, 05 Jul 2026 11:36:20 +0700

Postharvest tomato deterioration remains a major challenge due to manual and subjective quality assessment, which may lead to inconsistent sorting results and inaccurate shelf-life estimation. This study aims to develop a tomato quality classification system and predict potential shelf life based on digital image processing using the Random Forest algorithm. The study employed 936 tomato images and 450 non-tomato images collected independently. The extracted features consisted of Red Green Blue (RGB) and Hue Saturation Value (HSV) color features, as well as Gray Level Co-occurrence Matrix (GLCM) texture features. Tomato quality was classified into three categories, namely Poor, Medium, and Good, using a Random Forest Classifier, while shelf-life prediction was performed using a Random Forest Regressor. The classification model achieved an accuracy of 96.81%, precision of 96.82%, recall of 96.81%, and an F1-score of 96.81%. The regression model produced a Mean Absolute Error (MAE) of 0.0621, a Root Mean Square Error (RMSE) of 0.1152, and an R² value of 0.8752, while cross-validation yielded an average accuracy of 95.83% ± 1.24%, indicating stable model performance. Feature importance analysis revealed that color features contributed the most to both models, with g_mean identified as the most influential feature for tomato quality classification and shelf-life prediction. This study contributes to the development of a tomato quality assessment system capable of simultaneously classifying tomato quality and predicting shelf-life potential based on digital image processing using the Random Forest algorithm. In addition, feature importance analysis is employed to identify the visual characteristics that have the greatest influence on model performance. The results demonstrate that the proposed approach has the potential to support tomato sorting and postharvest management processes in a more objective and efficient manner.

Sistem Informasi Keselamatan dan Kesehatan Kerja Berbasis Web dengan Pemetaan Geografis untuk Deteksi Bahaya dan Pelaporan Insiden di Kampus

Adhwa Nabi, Ruminto Subekti, Cepi Ramdani — Sun, 05 Jul 2026 11:40:16 +0700

K3L management in vocational campuses still runs manually. Incident reports are filled out on paper forms, hazard data is stored in separate spreadsheets across units, and there is no single view showing where hazards are located. This study develops a web-based K3L information system for Politeknik Manufaktur Bandung using Laravel 12, MySQL, and Leaflet.js as an interactive GIS map engine, following a Research and Development (R&D) method across eight stages. The system has seven main features: (1) interactive GIS mapping with hazard markers on OpenStreetMap; (2) GPS-based incident reporting via Web Geolocation API with campus polygon boundary validation and accuracy display in meters; (3) hazard reporting with mandatory GPS and building floorplan overlay; (4) voice-to-text using Web Speech API in Bahasa Indonesia across four text fields (chronology, cause, first aid action, and hazard notes); (5) automatic WhatsApp notifications to reporters and task force when reports are submitted or status is updated; (6) GPS location verification by task force on incident reports; and (7) a knowledge center and emergency center accessible without login. Testing used black box testing via Newman (79 requests, 237 assertions, 0 failures), UI smoke testing (22 scenarios, 0 failures), and User Acceptance Testing (14 scenarios from three actors, all accepted). The system transforms the previously fragmented manual K3L workflow into a single centralized digital platform monitored in real-time. This study contributes a practical model through a web-based occupational health and safety information system that integrates interactive GIS mapping, GPS, and voice-to-text in one centralized platform, serving as a replicable reference for K3L digitalization in vocational campuses that can be adapted by other educational institutions.

Visualisasi Area Tanam Perkebunan Berbasis WebGIS Menggunakan Data Foto Udara Resolusi Tinggi

Ibrahim Rivalzi, Muhammad Ismail, Dedy Fitriawan, Eva Purnamasari — Sun, 05 Jul 2026 11:44:08 +0700

Spatial distribution and visualization of planting areas in PTPN IV Regional 4 Kayu Aro Unit were conducted using high-resolution aerial imagery and Geographic Information System (GIS). This study aims to identify the distribution of planting areas and develop a WebGIS as an interactive spatial visualization tool that can support the monitoring and management of plantation land. The methods used include visual interpretation of high-resolution aerial photos, land use digitization, GIS spatial analysis, and WebGIS implementation based on QGIS2Web. Land use classification distinguished planting and non-planting areas, supported by slope analysis to evaluate topographic influence on land utilization. Plantation areas are predominantly occupied by active planting zones, with the highest percentage recorded in Afdeling E (95.881%), followed by Afdeling G (92.735%), Afdeling F (92.689%), and Afdeling D (92.601%). Afdeling A shows the lowest planting proportion at 58.84%, indicating a relatively higher concentration of non-planting areas. Spatial patterns indicate that planting areas are generally distributed on flat to undulating slopes, representing more suitable conditions for cultivation and plantation management. Spatial data and attribute information were integrated into a WebGIS platform to support interactive visualization, spatial monitoring, and information accessibility. The research results show that Afdeling E has the highest planted area percentage at 95.88%, while Afdeling A has the lowest at 58.84%. Black Box testing shows that all WebGIS features work with a 100% success rate.

Peningkatan Keamanan Kunci Vigenère Menggunakan Steganografi Least Significant Bit (LSB) pada Sistem IoT Smart Door QR Code

Sun, 05 Jul 2026 11:48:08 +0700

In an increasingly connected digital era, the Internet of Things (IoT) enables seamless real-time data exchange across devices but also introduces critical challenges in data security. Although symmetric cryptography is widely adopted for its computational efficiency, key distribution and protection remain major vulnerabilities. This study aims to enhance IoT network security by integrating the Vigenère Cipher with Least Significant Bit (LSB) steganography. The LSB method is employed to conceal cryptographic keys within digital media, reducing the risk of unauthorized key interception. The proposed system is evaluated across three communication channels: the server, the user web interface, and ESP32/ESP32-CAM devices. Sniffing attack tests confirm that all transmitted data appears only as ciphertext, indicating successful protection of plaintext and secure key exchange. Performance measurements also demonstrate that the combined methods operate efficiently. On the server, encryption and encode require an average of 0.14 ms, while decode and decryption on the user web interface require 0.13 ms. On the ESP32-CAM, encryption and encode average 2.22 ms, with decode and decryption on the server requiring 0.10 ms. For the ESP32, server-side encryption and encode take 0.10 ms, while device-side decode and decryption take 1.46 ms. Overall, the integration of Vigenère Cipher and LSB steganography effectively improves data security in IoT communication without significantly impacting system performance.

Pemanfaataan Penginderaan Jauh dan Sistem Informasi Geografis Berbasis Transformasi Spektral Indeks Vegetasi Untuk Estimasi Produksi Tanaman Teh

Sun, 05 Jul 2026 11:54:38 +0700

Tea is a leading plantation commodity in Indonesia, but production estimation through manual field surveys has limitations in terms of cost, time, and accuracy, and is less able to describe spatial variations between blocks representatively. This study aims to estimate tea production in Afdeling B PTPN IV Danau Kembar using PlanetScope imagery with the Transformed Vegetation Index (TVI) approach, and to test the accuracy of the estimation compared to actual production data. The methods used include image pre-processing (radiometric calibration), TVI calculation, field data collection through sample plots, simple linear regression analysis, and production estimation at the block level using zonal aggregation and Jenks Natural Breaks classification. The results show that the TVI value ranges from 0.79–1.13 with a productive land area reaching 240 ha (84.21%) of the total 285 ha. The regression analysis yielded a coefficient of determination (R²) of 0.7933 with the equation y = 1388.8x – 1458.4, while the model validation results showed an R² of 0.7005. The total estimated tea shoot production in Afdeling B reached 119.5 tons, with the highest production in block 24 (8.80 tons) and the lowest in block 46 (0.48 tons). Although the model displayed good accuracy results at the sample scale, the resulting estimate was less precise compared to company data due to differences, namely the temporality of the data. However, the approach using TVI based on PlanetScope imagery has proven to have advantages in presenting spatial information on the distribution of tea plant productivity per block that cannot be obtained from conventional methods, thus supporting more efficient and sustainable spatial data-based tea plantation management. The contribution of this research is to provide a TVI- and PlanetScope-based tea production estimation model applied to the highland tea plantations of West Sumatra, while also generating a spatial productivity distribution map per block as a basis for more practical plantation management decision-making.

Pengembangan Model Deteksi Isu Publik Berbasis Latent Dirichlet Allocation Dengan Pendekatan Tren Waktu dan Analisis Sentimen pada Berita Online Nasional

Dhimas Bagus Prasetyo, Indah Susilawati — Sun, 05 Jul 2026 11:57:32 +0700

The growth of digital media and online news in Indonesia has generated a massive volume of information that continues to expand daily. This situation makes it difficult to identify public issues quickly and accurately, as manual news monitoring requires significant time, effort, and resources. Furthermore, the multitude of news sources with varying editorial focuses results in fragmented information that is challenging to analyze comprehensively. Consequently, an automated approach is needed to detect and monitor public issues within large datasets of online news. This study aims to develop a public issue detection model for national online news using the Latent Dirichlet Allocation (LDA) method. Research data was obtained via web scraping from CNBC Indonesia, Detik.com, Kompas.com, and Liputan6.com between January and December 2025, yielding 149,335 news headlines; after preprocessing, 146,557 clean data points remained. Topic modeling was performed using LDA, followed by analysis involving temporal trends, spike detection, media comparisons, and sentiment analysis based on the InSet dictionary. The results demonstrate that the LDA model successfully identified 16 key topics representing various public issues. The analysis revealed differences in reporting focus across media outlets, spikes in specific issues during certain periods, and a predominance of negative sentiment across most topics. These findings indicate that the proposed approach is capable of supporting the automated and structured monitoring of public issues.

Kuantifikasi Risiko Introspection pada Tiga Kategori Otorisasi OWASP: Studi Komparatif REST API dan GraphQL

Naufal Hanif Athallah, Galet Guntoro Setiaji, Ahmad Rifa’i — Sun, 05 Jul 2026 12:00:15 +0700

The advancement of Application Programming Interfaces (APIs) demands measurable architectural-level security evaluation. This study quantifies the security risks of REST API and GraphQL based on three authorization categories from the OWASP API Security Top 10 2023 (API1, API3, and API5). The exclusive limitation to these three categories was established to focus purely on access control logic flaws rather than infrastructure-level vulnerabilities. The experiment utilizes TixVuln, a parallel-architecture testbed instrument explicitly designed to eliminate external database bias a comparative advantage not present in standard single-architecture vulnerable applications. Authorization evaluation was executed contextually to avoid the high false-negative rates typically produced by automated security scanning tools (SAST/DAST) in business logic testing. Quantification results using the OWASP Risk Rating Methodology reveal a novelty that GraphQL experiences a risk category escalation from Medium to Critical levels in API3 and API5 compared to REST API. This significant leap in the Ease of Discovery metric is absolutely triggered by the operational schema exposure through the introspection feature. Mitigation testing validates that implementing field whitelisting and resolver-level Role-Based Access Control is imperative to suppress inherent risks in single-endpoint architectures. The main contribution of this research is the provision of an isolated empirical evaluation framework that quantitatively proves the flexibility of GraphQL architecture is directly proportional to the increased fatality of authorization risks if the schema discovery feature is not strictly configured.