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Management of Information System Journal
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 29649455     DOI : https://doi.org/10.47065
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
Management of Information System Journal merupakan jurnal yang mempublikasi hasil penelitian pada bidang Manajemen Informatika maupun Sistem Informasi, namun Management of Information System Journal dapat juga menampung kajian pada bidang Computer Science. Management of Information System Journal publish pada periode 4 bulanan, yaitu November (Issue 1), Maret (Issue 2) dan Juli (Issue 3). Management of Information System Journal memiliki ISSN 2964-9455 (media online) dengan no SK 29649455/II.7.4/SK.ISSN/12/2022.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 90 Documents
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Proyeksi Tren Kategori Pakaian Mendatang Menggunakan Random Forest pada Data Transaksi Pelanggan Nur Aini Umar; Andi Ircham Hidayat Hidayat; Eka Wijaya Paula
Management of Information System Journal Vol 4 No 3: Juli 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i3.2776

Abstract

The dynamic fashion industry requires accurate trend projections for marketing and product development. This study aims to project future clothing trends using Random Forest as the primary model and XGBoost as the secondary model. The main dataset contains 3,900 transactions with demographic information, purchase history, seasonal data, and product categories. For local context, inventory data from the “Coffer Ruh” fashion store was integrated as a companion case study. The methodology included preprocessing, handling class imbalance with SMOTE, stratified splitting (80:20), training Random Forest and XGBoost, and evaluation using accuracy, precision, recall, F1-score, and a confusion matrix. Evaluation results (Outerwear, Footwear, Bottoms, Tops, Accessories) show that Random Forest achieved an accuracy of 67.56%, weighted precision of 68.04%, recall of 67.56%, and an F1-score of 67.79%, while XGBoost demonstrated similar performance with an accuracy of approximately 68%. The Random Forest model projected Jackets (17%), Coats (12%), and Shoes (10%) as the top three global trend categories. Store data analysis revealed the highest stock levels for children’s masks (35 pcs), red cornersticks (33 pcs, coats), and drams (31 pcs, jackets). There is some alignment: two of the three products with the highest inventory are outerwear items that align with global trends; however, masks are not a predicted apparel category. Due to limitations in the store data (small sample size, lack of time/transaction dimensions), transfer learning or hybrid dataset approaches cannot yet be applied, which is identified as a limitation and a direction for future research.
Analisis Sentimen Ulasan Aplikasi Mobile Legends Berbasis Pelabelan IndoBERT dan SMOTE dengan Komparasi Algoritma Klasifikasi Naive Bayes dan SVM Muhammad Fauzan Aditiya Mufid; Erna Daniati; Arie Nugroho
Management of Information System Journal Vol 4 No 3: Juli 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i3.2783

Abstract

This study was conducted to evaluate user sentiment trends toward the Mobile Legends: Bang Bang game app through reviews published on the Google Play Store platform. This research applied IndoBERT-based automatic labeling to generate sentiment labels that better represent the textual meaning of the reviews. The class distribution imbalance resulting from the labeling process was addressed using the Synthetic Minority Oversampling Technique (SMOTE) during the model training phase. The performance of two classification algorithms—the Naive Bayes Classifier and the Support Vector Machine (SVM)—was compared through hyperparameter tuning using GridSearchCV, with the F1-Macro metric. The results show that the Naive Bayes Classifier model delivers the best performance with an accuracy of 87.55%, an F1-Score of 85.78%, and an F1-Score of 56.70%. However, the model still exhibits significant limitations in recognizing the neutral sentiment class (F1-Score 0.21), which was further analyzed and found to be caused by the very small proportion of the neutral class (2.3% of the total data) as well as the characteristic of neutral reviews, which tend to be requests or suggestions to developers rather than explicit statements. This study contributes to the development of a more representative sentiment analysis methodology through a combination of transformer-based labeling, data imbalance handling, and classification algorithm comparison.
Evaluasi Kesuksesan Sistem TokSort Pada UMKM Devline Store Menggunakan Model DeLone dan McLean Aditya Arya Respati; Erna Danianti; Aidina Ristyawan
Management of Information System Journal Vol 4 No 3: Juli 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i3.2785

Abstract

Manual order data management in small and medium enterprises may cause work delays, recording errors, and difficulties in monitoring order completion. This study evaluates the success of TokSort as a mobile-based information system for processing Comma Separated Values (CSV) order data at Devline Store using an adapted DeLone and McLean IS Success Model. A descriptive quantitative approach with an evaluative case study framing was applied. The respondents were 11 internal TokSort users directly involved in order data management, so total sampling was used. Data were collected through a Likert-scale questionnaire and observation, then analyzed using mean values, validity testing, reliability testing, and exploratory Spearman Rank correlation. The results show that all variables were classified as good: system quality 3.42, information quality 3.64, use 3.86, user satisfaction 3.59, and net benefits 3.73. Spearman Rank results indicate positive and significant relationships among the tested variables. These findings show that TokSort is considered successful in supporting order data processing, grouping, and monitoring based on internal users’ perceptions. However, system stability and the ability to reduce order processing errors still require improvement. This study contributes an empirical evaluation of an internal SME operational information system based on CSV file processing.
Sistem Pendukung Keputusan Untuk Pemilihan Kualitas Telur Bebek Di Kabupaten Nganjuk Menggunakan Metode SAW Angga Pradipa Eko Widodo; Muhammad Najibullah Muzaki; Erna Daniati
Management of Information System Journal Vol 4 No 3: Juli 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i3.2798

Abstract

The determination of duck egg quality among farmers and salted egg SMEs in Nganjuk Regency is still carried out manually based on experience, resulting in subjective, inconsistent, and time-consuming assessments. This condition causes the egg grading process to be less optimal and may affect the quality of products marketed. This study aims to design and develop a web-based Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to support a more objective, faster, and structured duck egg quality selection process. The system was developed using the Waterfall model, consisting of requirements analysis, system design, implementation, and testing stages. The assessment was based on four criteria: egg weight (35%), egg size (10%), storage time (25%), and eggshell color scale (30%). The system was implemented using Google Apps Script with Google Spreadsheet as the database. The implementation results show that the system is able to perform normalization, weighting, preference value calculation, and automatic ranking of duck egg quality. Blackbox Testing on seven main modules, namely login, alternative data, criteria data, weight setting, SAW calculation, ranking results, and report printing, showed that all modules functioned according to user requirements with a success rate of 100%. The results indicate that the SAW method can support duck egg quality assessment more objectively, consistently, and efficiently than manual assessment, thereby assisting decision-making for farmers and salted egg SMEs in Nganjuk Regency.
Penerapan Sistem Lampu Jalan Automatis Berbasis Sensor Cahaya Dengan Integrasi IoT Mhd Rizki Syahputra; Andri Syahputra; Mangiring Hokkop Kristofer Sitinjak; Fitri Febriadi Turnip; Muhammad Habib
Management of Information System Journal Vol 4 No 3: Juli 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i3.2826

Abstract

The development of Internet of Things (IoT) technology provides significant opportunities in automation systems, including public street lighting systems. Manual operation of street lights often causes energy waste because the lights remain active when unnecessary. This research aims to design and implement an automatic street lighting system based on light sensors integrated with IoT using NodeMCU ESP8266 and Light Dependent Resistor (LDR) sensors. The system is designed to automatically control the lights based on environmental light intensity and provide real-time monitoring through the Blynk application. The research method used is an experimental method involving hardware design, programming, implementation, and testing stages. The results show that the system can accurately detect changes in light intensity and automatically control the street lights according to environmental conditions. In addition, monitoring data can be displayed in real-time through the Blynk application via internet connectivity. The system also improves energy efficiency because the lights only operate when needed. Based on testing results, the system works properly, stably, and has potential applications in smart lighting and smart city infrastructure.
Analisis Business Intelligence Ulasan Negatif Aplikasi M-Pajak Menggunakan BERTopic untuk Evaluasi Layanan Digital Henry Pandia
Management of Information System Journal Vol 4 No 3: Juli 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i3.2848

Abstract

The M-Pajak application is part of the government’s digital tax service transformation aimed at improving the efficiency and accessibility of tax services for the public. However, user complaints on the Google Play Store indicate that technical and operational problems persist with the application. This study aims to analyze negative user reviews of the M-Pajak application using BERTopic to generate Business Intelligence insights related to the quality of government digital tax services. BERTopic was selected because it uses transformer-based contextual embeddings, which are well-suited to analyzing short, unstructured, and informal mobile application reviews, thereby helping identify complaint themes in a more context-aware way than traditional word-distribution-based topic modeling approaches. The data were collected from Google Play Store reviews from May 2023 to May 2026, totaling 6,024 user reviews. A total of 5,008 negative reviews with ratings of 1 or 2 were used as the primary focus of the analysis. The research stages included data scraping, preprocessing, BERTopic modeling, topic evaluation, and Business Intelligence interpretation. The results show that the main user problems are related to authentication and login, system errors, email and OTP verification, NPWP and NIK registration, and digital service stability. Model evaluation yielded a Topic Coherence score of 0.522, a Topic Diversity score of 0.667, and a Topic Quality score of 0.348, indicating that the topic quality was moderate yet interpretable. The contribution of this study lies in utilizing negative reviews of a digital tax service application as a source of operational insights to support the evaluation and improvement of government digital public services.
Prediksi Harga Rumah Berbasis Machine Learning dengan Explainable AI untuk Interpretabilitas Faktor Penentu Hadijah; Wiwin Handoko; Rizty Maulida Badri
Management of Information System Journal Vol 4 No 3: Juli 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i3.2860

Abstract

House prices are determined by numerous interrelated factors, making it essential to develop prediction methods that are not only accurate but also interpretable by property business practitioners, investors, and policymakers. This study aims to construct a house price prediction model using a machine learning approach integrated with Explainable Artificial Intelligence (XAI) to produce predictions that are more transparent and comprehensibly interpretable. The data used in this study were derived from real property listings, incorporating several key variables including building area, land area, number of bedrooms, number of bathrooms, and garage capacity. Four machine learning algorithms were evaluated and compared, namely Linear Regression, Random Forest, XGBoost, and Gradient Boosting. The performance of each model was assessed using multiple evaluation metrics, comprising Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), coefficient of determination (R²), and Mean Absolute Percentage Error (MAPE). Experimental results demonstrate that the Random Forest algorithm achieved the best performance, yielding an R² value of 0.7636, MAE of IDR 1.305 billion, RMSE of IDR 2.037 billion, and MAPE of 25.84%. The best-performing model was subsequently analyzed using SHapley Additive exPlanations (SHAP) to provide both global and local model interpretability, as well as Local Interpretable Model-agnostic Explanations (LIME) to explain individual predictions at the instance level. The analysis reveals that building area and land area are the most influential factors in determining house prices. The proposed approach demonstrates a measurable improvement in model transparency, rendering prediction outcomes more comprehensible and trustworthy for end users.
Analisis Kepuasan Masyarakat Terhadap Pelayanan E-Ktp di Kantor Kecamatan Kembang Janggut Menggunakan Metode Servqual Nur Syariffah; Ivan Haristyawan; Rizky Zakariyya Rasyad
Management of Information System Journal Vol 4 No 3: Juli 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i3.2863

Abstract

E-KTP service is a form of public service that must be able to meet public expectations. However, various obstacles remain, such as the lengthy service process, lack of officer responsiveness, and technical constraints that have the potential to reduce public satisfaction. This study aims to analyze the level of public satisfaction with E-KTP services at the Kembang Janggut District Office using the SERVQUAL method. The study used a quantitative approach involving 83 respondents selected using the Slovin formula. Data were obtained through distributing questionnaires based on five SERVQUAL dimensions, namely tangibles, reliability, responsiveness, assurance, and empathy, then analyzed using the calculation of the gap value between public perceptions and expectations. The results showed that all dimensions had a negative gap value, which means the quality of service has not fully met public expectations. The responsiveness dimension had the largest gap value of -0.22, making it a top priority in service improvement, while the empathy dimension had the smallest gap value of -0.13, indicating that the attitude and attention of officers have been rated the best by the public. This research provides a contribution in the form of identifying service dimensions that are priority for improvement so that it can be a basis for the Kembang Janggut District Office in developing strategies to improve the quality of E-KTP services and increase public satisfaction.
Deteksi Real-Time Hama dan Penyakit Jamur pada Daun Tanaman Menggunakan Deep Learning Berbasis YOLO Reni Yunita; Egi Dio Bagus Sudewo; Bela Astuti
Management of Information System Journal Vol 4 No 3: Juli 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i3.2882

Abstract

Serangan hama pertanian dapat menurunkan produksi tanaman secara signifikan dan menghambat kegiatan pertanian. Kesulitan mendeteksi hama secara manual sejak tahap awal membuat petani sering menggunakan pestisida secara berlebihan, yang dapat menyebabkan pencemaran lingkungan dan risiko terhadap kesehatan. Untuk mengatasi permasalahan tersebut, berbagai sistem telah dikembangkan untuk membantu mendeteksi hama sejak dini sehingga petani dapat mengetahui lokasi hama secara lebih tepat. Namun, proses identifikasi di lapangan yang masih dilakukan secara manual sering memerlukan waktu lama, tenaga yang besar, serta rentan terhadap kesalahan identifikasi. Selain itu, beberapa sistem yang telah dikembangkan masih memiliki keterbatasan, seperti belum mampu melakukan deteksi secara real-time dan belum dilengkapi dengan sistem pemantauan berbasis web. Penelitian ini mengusulkan sistem deteksi hama dan penyakit jamur pada daun tanaman secara real-time menggunakan pendekatan deep learning berbasis arsitektur YOLO26. Model YOLO26 dipilih karena memiliki kemampuan deteksi objek yang cepat dan efisien sehingga cocok untuk aplikasi pemantauan pertanian secara real-time. Dataset yang digunakan terdiri dari citra daun tanaman yang telah dianotasi ke dalam dua kelas objek, yaitu pest dan fungus. Hasil pengujian menunjukkan bahwa model yang diusulkan mampu mencapai precision sebesar 82%, recall sebesar 71%, dan mAP@0.5 sebesar 78%, dengan nilai mAP@0.5–0.95 sebesar 37,4%. Selain itu, model memiliki waktu inferensi sekitar 12,1 ms/citra, sehingga mampu melakukan deteksi secara real-time. Secara keseluruhan, sistem yang dikembangkan berpotensi membantu petani dalam melakukan pemantauan tanaman secara otomatis dan mendeteksi serangan hama serta penyakit jamur sejak dini, sehingga dapat mengurangi kerusakan tanaman, menekan penggunaan pestisida secara berlebihan.
Sistem Deteksi Jatuh Lansia Real-Time Berbasis YOLOv8 dengan Notifikasi Telegram dan Dashboard Web Claudio Syanu Mareta Dinata; Rina Firliana; Arie Nugroho
Management of Information System Journal Vol 4 No 3: Juli 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i3.2885

Abstract

Falls in the elderly represent a serious global health problem. According to the WHO, one in three elderly people aged over 65 years experiences a fall each year. In Panti Werda Kediri, monitoring of elderly activities is still performed manually by staff, causing fall incidents to go undetected quickly. Previous YOLO-based fall detection studies generally produce models without integrating them into monitoring platforms usable by non-technical end users and without automatic notification. This research aims to determine the effectiveness of a monitoring system in detecting normal activities and fall incidents in elderly residents in real time at Panti Werda Kediri using YOLOv8. The system was developed using the Waterfall method through stages of requirements analysis, system design, implementation, and testing. The detection component uses a retrained YOLOv8 model to recognize two classes: normal and fall. The backend is built with FastAPI and PostgreSQL, equipped with a web-based monitoring dashboard and automatic notifications via Telegram Bot. A fall confirmation mechanism based on 3 consecutive frames with a 1.5-second cooldown suppresses false positives. Blackbox testing conducted at Panti Werda Kediri shows all 10 test scenarios passed. The system successfully sends real-time Telegram notifications in under 2 seconds with visual evidence each time a fall is confirmed, provides live camera streaming, and displays complete detection history through a web dashboard accessible to non-technical staff.

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