cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
jurnal.josh@gmail.com
Phone
+6282161108110
Journal Mail Official
jurnal.josh@gmail.com
Editorial Address
Sekretariat Forum Kerjasama Pendidikan Tinggi (FKPT) Jalan Sisingamangaraja No. 338, Medan, Sumatera Utara
Location
Kota medan,
Sumatera utara
INDONESIA
JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 2686228X     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Artikel yang dimuat melalui proses Blind Review oleh Jurnal JOSH, dengan mempertimbangkan antara lain: terpenuhinya persyaratan baku publikasi jurnal, metodologi riset yang digunakan, dan signifikansi kontribusi hasil riset terhadap pengembangan keilmuan bidang teknologi dan informasi. Fokus Journal of Information System Research (JOSH)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 870 Documents
 83   84   85   86   87 
Optimasi Model Retrieval-Augmented Generation Menggunakan Algoritma Indeks HNSW Lokal pada Small Language Model untuk Mitigasi Halusinasi Hadis Bukhari Rizqi Ari Putra; Suhartono Suhartono; Muhammad Faisal
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10467

Abstract

Small Language Models (SLMs) offer high local computational efficiency but possess a systemic vulnerability to information hallucination. This vulnerability becomes a critical risk when the model is applied to sensitive domains demanding absolute accuracy, such as Sharia law and Islamic sacred literature. The inaccurate citation of religious texts can lead to theological misguidance. To address this issue, this study proposes a low-memory, local Retrieval-Augmented Generation (RAG) architectural solution. This system is designed by integrating the open-source Llama-3.2-1B-Instruct model with the PostgreSQL pgvector vector database. Document retrieval optimization is performed based on the HNSW (Hierarchical Navigable Small World) indexing algorithm and a 16-bit precision quantization technique (halfvec) on 7,003 chunks of the Sahih al-Bukhari Hadith corpus. The primary objective of this research is to design a high-precision hallucination mitigation system that operates independently (on-premise), while making a tangible contribution to the development of a low-cost digital theological assistant that preserves privacy and data sovereignty. Mitigation efficacy was automatically evaluated using the Ragas framework against 50 theological test queries, while database efficiency was physically tested on consumer-grade local computer hardware. Experimental results indicate that the proposed architecture is capable of significantly improving the faithfulness metric by 117.4% (from 0.4120 to 0.8960) and answer relevance by 50.0% (from 0.6120 to 0.9180), while simultaneously accelerating inference response time by up to 50.7%. On the database side, halfvec quantization successfully reduced physical table storage space by 36.2% and accelerated index construction time by 16.5% with an absolute accuracy (recall) retention rate of 1.0000. This study proves that a high-precision religious virtual assistant is highly feasible to execute independently without relying on third-party cloud computing services.
Design and Development of a Web-Based Student Discipline Evaluation Information System Using a Research and Development Approach Hendra Parsaulian; Donny Maulana; Annisa Maulana
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10469

Abstract

Student discipline management is an essential component of educational administration that supports the creation of a conducive learning environment. However, disciplinary management at SMA Negeri 2 Cikarang Selatan was previously conducted manually using notebooks and spreadsheet applications, resulting in data redundancy, inaccurate calculation of violation points, delays in report generation, and difficulties in monitoring students' disciplinary records. This study aims to design and develop a Web-Based Student Discipline Evaluation Information System to improve the efficiency, accuracy, and transparency of disciplinary management. The study employed the Research and Development (R&D) approach, which consisted of needs analysis, system design, system development, implementation, and system evaluation. The proposed application was developed using PHP and MySQL and integrated several functional modules, including user authentication, student data management, violation recording, automatic point calculation, report generation, and user management. Functional evaluation was conducted using the Black-Box Testing method to verify that each module operated according to the specified requirements. The evaluation results indicated that all functional modules performed successfully without significant errors, demonstrating that the developed system satisfied the identified functional requirements. Compared with the previous manual procedure, the proposed system improved administrative efficiency, enhanced data accuracy through automated point calculation, facilitated faster report generation, and increased accessibility to disciplinary information through a centralized web-based platform. The developed system also strengthened communication among administrators, teachers, guidance counselors, and student guardians by providing accurate and timely disciplinary information. Therefore, the proposed Web-Based Student Discipline Evaluation Information System can serve as an effective digital solution for modernizing student discipline management and supporting evidence-based decision-making in educational institutions.
Perancangan Sistem Absensi Berbasis Internet of Things Menggunakan RFID dan Arduino Fitriyani Fitriyani; Tia Luthfia Annisa; Debi Irawan; Fauzan Daffa
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10477

Abstract

This study discusses the design of an Internet of Things (IoT)-based attendance system using RFID and NodeMCU ESP8266 to support and improve the efficiency of teacher attendance management at MTs Al-Ikhlas Cicalengka. The primary problem motivating this study is the reliance on a conventional manual attendance system, which is highly vulnerable to recording errors (human error), time inefficiencies during daily data recapitulation, and the risk of physical document loss or damage. Therefore, digital transformation is urgently needed. The proposed system integrates several hardware components: RFID RC522 as the identity card reader, NodeMCU ESP8266 as the main controller and internet (Wi-Fi) connector, and a 16x2 LCD as an interactive information display. The attendance data is directly transmitted and stored in a cloud-based Google Spreadsheet. The system was programmed using Arduino IDE to operate automatically. This study utilized a design and prototyping method by testing 10 registered RFID cards belonging to the teachers. The test results demonstrated that all cards were successfully read, and attendance data were accurately stored in Google Spreadsheet with a 100% success rate. The average system response time was recorded at a very fast 2.2 seconds. In conclusion, the implementation of this IoT-based attendance system is proven to be effective in minimizing data manipulation, accelerating the recording process, and generating attendance recapitulations that are more transparent and easily accessible. The contribution of this research is providing a practical, affordable automation system that can serve as a reference model for other secondary educational institutions in digitally transforming their administrative management.
Sentiment Analysis of YouTube Comments on Indonesia-U.S. Trade Agreement: A Comparison of Machine Learning and Deep Learning Dea Amanda; Muhammad Iqbal; Mia Rosmiati
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10515

Abstract

The Indonesia–United States Trade Agreement has sparked much public debate on YouTube, but due to the large volume of data and the informal nature of the text, manual analysis is ineffective. Through a comparative study of machine learning (Naïve Bayes and Support Vector Machine) and deep learning (Long Short-Term Memory and IndoBert), this research aims to identify the polarity of public opinion and evaluate the best computational model. This study contributes a comprehensive empirical comparison of the four algorithms within an identical experimental pipeline on an issue that has not been previously explored, thereby offering methodological insights for future sentiment analysis research on similarly informal, domain-specific social media text. The dataset consists of 2,284 comments labeled using the InSet Lexicon. The analysis results show that the neutral class dominates the sentiment distribution (52,0%), followed by the positive class (20,2%) and the negative class (27,8%). Performance evaluation reveals that IndoBERT significantly outperforms other models with an accuracy of 87.71% and a Macro F1-Score of 0.87. Support Vector Machine ranked second (79.00%), followed by Naïve Bayes (61.71%). In contrast, Long Short-Term Memory failed completely (accuracy of 52.00%) due to the “majority class collapse” phenomenon in small-scale datasets. This study concludes that the Transformer architecture (IndoBert) is the most robust approach for classifying sentiment in informal Indonesian-language text containing specific geopolitical and economic terms.
Analisis Kontribusi Sensor IoT pada Deteksi Kebakaran Lahan Gambut Menggunakan Random Forest dan SHAP Hirzen Hasfani; Kartika Sari; Rahmi Hidayati
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10516

Abstract

Peatland fires are disasters that impact the environment, health, and socio economic activities. Internet of Things (IoT) based early detection enables real-time monitoring of environmental conditions through various sensors. However, the specific contribution of each sensor to the detection process remains unclear. This study aims to analyze the contribution of multiple sensors within an IoT-based peatland fire detection system using the Random Forest (RF) algorithm. The dataset comprises 2,000 primary data points obtained from AMG8833, MAX6677, MQ-2, DHT22, and Water Float sensors. The model was trained on the primary data and tested against data representing transitional (overlapping) conditions between normal states and fire events. Model performance was evaluated using accuracy, precision, recall, F1-score, and a confusion matrix, while sensor contributions were analyzed via Feature Importance and validated using SHapley Additive exPlanations (SHAP). The results indicate that the RF model achieved an accuracy of 87.50%, precision of 100.00%, recall of 75.00%, and an F1-score of 85.71%. Feature Importance and SHAP analyses revealed that the DHT22 sensor (measuring humidity and temperature) made the most significant contribution, followed by the MAX6677, MQ-2, AMG8833, and Water Float sensors. These findings demonstrate that temperature and humidity serve as key indicators for peatland fire detection and provide a foundation for developing IoT-based detection systems.
Optimalisasi Hyperparameter Random Forest Menggunakan Random Search untuk Klasifikasi Risiko Stunting Pada Balita Putri Windari; Khairunnas Khairunnas; Irma Eryanti Putri
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10531

Abstract

Stunting is a condition of impaired child growth resulting from chronic nutritional deficiency over an extended period. This condition not only hinders physical growth but also impedes learning abilities and increases the risk of various future diseases. The high prevalence of stunting in Bima City highlights the need to utilize Machine Learning methods to analyze stunting risk factors more accurately. This study aims to optimize the performance of the Random Forest algorithm using the Random Search method to classify stunting risk among children under five in Sambinae Urban Village, Bima City. The dataset comprises records for 1,162 children under five, featuring 20 attributes obtained from the Mpunda Community Health Center (Puskesmas) in Bima City. The research stages include data collection, preprocessing, data splitting, construction of a baseline Random Forest model, hyperparameter optimization using Random Search, evaluation via a Confusion Matrix (based on Accuracy, Precision, Recall, and F1-Score), and feature importance analysis. Prior to optimization, the baseline Random Forest model yielded an accuracy of 70,39%, precision of 56,67%, recall of 62,96%, and an F1-score of 59,65%. Following optimization with Random Search, model performance improved to an accuracy of 71,67%, precision of 58,43%, recall of 64,20%, and an F1-score of 61,18%. The results demonstrate that hyperparameter optimization using Random Search effectively enhances the Random Forest model's performance in classifying stunting risk. The study contributes a stunting risk classification model based on the Random Forest algorithm, optimized via Random Search to achieve a more effective hyperparameter combination than the default settings. Furthermore, the study provides a comparative performance analysis before and after optimization, along with insights into the variables that most significantly influence stunting risk classification. These findings are expected to assist healthcare professionals and local government authorities in identifying stunting risks more rapidly and accurately, thereby serving as a foundation for formulating more targeted prevention strategies.
Smart Story Archive for WhatsApp Status: A Design Thinking-Based Model for Private Status Retrieval Nadya Lathifah Riady; Wahyu Kusuma Putra; Rizky Kwarta Ardhana
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10532

Abstract

WhatsApp Status supports quick sharing of daily moments, yet its 24-hour visibility creates a gap between temporary public sharing and private long-term retrieval. Existing story features mainly emphasize posting and viewing, while private retrieval by date, person, location, keyword, media type, favorite mark, and download access remains limited in WhatsApp Status. This study proposes Smart Story Archive as a user-centered design concept for private Status retrieval. The method used Design Thinking with survey-based needs analysis involving 100 experienced WhatsApp users, selected because they actively used WhatsApp Status and could evaluate archive-related pain points. This study contributes a theoretical and empirical framework for ephemeral data retrieval in private messaging environments by introducing a conceptual model that reconciles user desires for both ephemerality and long-term memory accessibility. The data were analyzed using descriptive statistics and translated into feature requirements and a high-fidelity prototype in Google Stitch. The key findings show three dominant needs: users want to recover meaningful Status memories, retrieve content through incomplete memory cues, and maintain privacy control over archived metadata. The proposed design addresses these needs through multi-cue search, optional private archiving, metadata control, and deletion access. This study contributes a WhatsApp-specific archive model that balances memory retrieval, privacy, and interface simplicity for future usability testing.
Rancang Bangun Sistem Informasi Manajemen Barang dengan Fitur Pendukung Keputusan Prioritas Kebutuhan Barang Menggunakan Metode AHP-SAW Arif Mardiyansyah; Agariadne Dwinggo Samala; Ahmaddul Hadi; Yulia Fatmi; Widya Darwin
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10562

Abstract

Inventory management in government agencies often faces obstacles in determining procurement proposal priorities due to manual recording processes and subjective judgment. This study aims to design and build a web-based Inventory Management Information System integrated with a Decision Support System (DSS) using the AHP–SAW method at the Padang City Transportation Office. The system development method used is Waterfall, with the Laravel framework and MySQL database as the technological foundation. The integration of the AHP method is used to determine the criteria importance weights, while the SAW method is used to rank ten operational items alternatives. The results showed that operational urgency became the most dominant parameter with a weight of 0.557. The AHP calculation results were declared consistent with a Consistency Ratio (CR) value of 0.043. The ranking results successfully produced an objective priority sequence of goods requirements based on the combination of criteria weights and alternative values. Functional testing and comparison tests prove that the system produces outputs identical to manual calculations. The implementation of this system has successfully increased warehouse administration efficiency and provided a measurable recommendation instrument for agency leaders in preparing operational goods requirement proposals. The contribution of this study is the integration of an Inventory Management Information System with the AHP–SAW method into a single web-based platform, enabling operational inventory data to be utilized not only for inventory management but also as the basis for determining goods requirement priorities in a more objective and data-driven manner.
Sentiment Analysis Kepercayaan Publik Terhadap Pertamina Pada Media Berita di YouTube Alrafi Syammajaya; Daniel H. F. Manongga
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10574

Abstract

Public trust in PT Pertamina (Persero) is reflected in public opinion expressed on social media, particularly through the comment sections of news videos on YouTube. This study aims to analyze public sentiment toward Pertamina based on comments posted on YouTube news videos in order to identify the distribution of positive, negative, and neutral opinions. The data were collected using the YouTube Data API through a web crawling process and subsequently processed using several text preprocessing techniques, including cleaning, case folding, word normalization, tokenization, and stopword removal. Sentiment labeling was performed automatically using the VADER Sentiment method, which classified the comments into three categories: positive, negative, and neutral. Feature extraction was then conducted using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The classification process compared the performance of three Naïve Bayes variants, namely Gaussian Naïve Bayes, Multinomial Naïve Bayes, and Bernoulli Naïve Bayes. Of the 56,868 comments that were successfully crawled, 22,527 comments were successfully collected. The sentiment labeling results revealed that neutral sentiment dominated the dataset, accounting for 95.89% of all comments, followed by positive sentiment at 2.86% and negative sentiment at 1.25%. The experimental results demonstrated that Multinomial Naïve Bayes achieved the best performance with an accuracy of approximately 95%, outperforming Bernoulli Naïve Bayes (approximately 92%) and Gaussian Naïve Bayes (approximately 79%). This superior performance is attributed to the compatibility of the Multinomial Naïve Bayes algorithm with TF-IDF feature representation, which is based on word frequency. The findings of this study are expected to provide valuable insights for Pertamina and policymakers in formulating more responsive and effective public communication strategies.
Klasifikasi Penyakit Daun Bawang Merah Menggunakan MobileNetV2 Convolutional Neural Network (CNN) Widia Ainun Arabiah; Fathir Fathir; Hilyatul Mustafidah
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10575

Abstract

Diseases affecting shallot plants are a primary cause of reduced crop quality and yield. Manual disease identification relies on visual observation, making it prone to error and time-consuming. This study aims to develop a classification model for shallot leaf diseases by combining Gray Level Co-occurrence Matrix (GLCM) feature extraction with MobileNetV2, classified using a Convolutional Neural Network (CNN). The dataset comprises 1,188 shallot leaf images categorized into five classes: downy mildew, healthy, leaf blight, *moler* (basal rot), and purple blotch. The research process involved dataset collection; pre-processing (image resizing to 224×224 pixels, grayscale conversion, normalization, and data augmentation); texture feature extraction using GLCM; and deep feature extraction using MobileNetV2. These features were then combined and used as input for the CNN classification model. Model evaluation was conducted using a confusion matrix, assessing accuracy, precision, recall, and F1-score. The results demonstrate that the proposed model achieved 93% accuracy, with balanced precision, recall, and F1-score values ​​across most classes. The contribution of this research is the integration of gray level co-occurrence matrix (GLCM) texture feature extraction with mobilenetv2 visual features within a convolutional neural network (CNN) model to improve the classification performance of shallot leaf diseases.

Page 85 of 87 | Total Record : 870

 83   84   85   86   87 

Filter by Year

2019 2026


Reset
Filter By Issues
All Issue Vol 7 No 4 (2026): July 2026 Vol 7 No 3 (2026): April 2026 Vol 7 No 2 (2026): January 2026 Vol 7 No 1 (2025): October 2025 Vol 6 No 4 (2025): July 2025 Vol 6 No 3 (2025): April 2025 Vol 6 No 2 (2025): January 2025 Vol 6 No 1 (2024): Oktober 2024 Vol 5 No 4 (2024): Juli 2024 Vol 5 No 3 (2024): April 2024 Vol 5 No 2 (2024): Januari 2024 Vol 5 No 1 (2023): Oktober 2023 Vol 4 No 4 (2023): Juli 2023 Vol 4 No 3 (2023): April 2023 Vol 4 No 2 (2023): January 2023 Vol 4 No 1 (2022): October 2022 Vol 3 No 4 (2022): Juli 2022 Vol 3 No 3 (2022): April 2022 Vol 3 No 2 (2022): Januari 2022 Vol 3 No 1 (2021): Oktober 2021 Vol 2 No 4 (2021): Juli 2021 Vol 2 No 3 (2021): April 2021 Vol 2 No 2 (2021): Januari 2021 Vol 2 No 1 (2020): Oktober 2020 Vol 1 No 4 (2020): JULI 2020 Vol 1 No 3 (2020): April 2020 Vol 1 No 2 (2020): Januari 2020 Vol 1 No 1 (2019): Oktober 2019 More Issue