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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Ilmiah Media Sisfo JOURNAL V-TECH (VISION TECHNOLOGY) Account : Jurnal Akuntansi, Keuangan dan Perbankan Jurnal Teknik Informatika (JUTIF) Quantitative Economics and Management Studies Journal of Applied Accounting And Business (JAAB) Journal of Trends Economics and Accounting Research Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Ilmiah Manajemen dan Kewirausahaan Modem : Jurnal Informatika dan Sains Teknologi Jurnal Kajian Hukum Dan Kebijakan Publik
Gunardi Gunardi
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

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An Adaptive Feature-Aware Hybrid Resampling Strategy for Imbalanced Diabetes Classification with Integrated Balanced Index Evaluation Jasmir, Jasmir; Pahlevi, Riza; Gunardi, Gunardi; Rohaini, Eni; Annisa, Tiko Nur
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7418

Abstract

Class imbalance remains a critical challenge in medical data classification, particularly in diabetes prediction, as it significantly degrades minority-class sensitivity. This study proposes an Adaptive Feature-Aware Hybrid Resampling Strategy (AHRS) that dynamically integrates oversampling and undersampling based on Imbalance Ratio (IR) and Feature Importance (FI). Unlike conventional static resampling methods, AHRS iteratively adjusts class distribution while preserving informative feature structures. In addition, this study introduces the Integrated Balanced Index (IBI), a bounded composite metric integrating precision, recall, and specificity to provide a fairer evaluation of classification performance on imbalanced medical datasets. The proposed approach was evaluated using the Pima Indian Diabetes Dataset (768 instances) with K-Nearest Neighbor, Naïve Bayes, and Random Forest classifiers under 5-fold stratified cross-validation. Experimental results demonstrate that AHRS consistently outperforms SMOTE, Random Oversampling, and Tomek Links, achieving accuracy improvements of 5–7% and recall gains of up to 10%. Random Forest combined with AHRS achieved the highest IBI score of 0.90, indicating strong balance between sensitivity and specificity. The findings suggest that adaptive, feature-aware resampling combined with balanced evaluation metrics provides a reliable and interpretable framework for fair medical classification systems and Clinical Decision Support Systems (CDSS).
SCRUM AND ITIL-BASED SUPPORT SYSTEM DESIGN AND IMPLEMENTATION AT RAPHA THERESIA HOSPITAL Kasrizal Kasrizal; Sharipuddin Sharipuddin; Joni Devitra; Gunardi Gunardi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7004

Abstract

This research addresses the challenges of manual IT service management at Rapha Theresia Hospital, where existing processes lacked systematic tracking and reporting, leading to operational inefficiencies. The purpose was to design and implement a web-based IT support system for systematic documentation of IT requests and repairs, integrating the Scrum agile development methodology with the ITIL framework, and enabling comprehensive IT performance reporting for management evaluation. The study employed a hybrid methodological approach, combining Scrum for iterative development and ITIL for robust service delivery. Research methods included problem identification, and iterative implementation across four sprints with defined Service Level Agreements (SLAs). Rigorous User Acceptance Testing (UAT) validated the system's functionality. Results show successful implementation of a centralized system managing IT requests, assets, and reports, significantly improving operational efficiency, service reliability, and fostering data-driven decision-making. The system enhanced coordination, transparency, and accelerated service resolution within the IT team.
Improving Bioethanol Sentiment Analysis Performance using SMOTE in Machine Learning Model Comparison Rajhu Ilham Pradana; Jasmir Jasmir; Gunardi Gunardi
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6300

Abstract

Sentiment analysis of public policies on social media is crucial for government evaluation; however, it is often challenged by highly imbalanced datasets. This study aims to address this issue through a case study on public sentiment toward bioethanol fuel policies on YouTube, where the cleaned dataset after preprocessing consisted of 2,409 comments dominated by negative sentiment (1,430 comments), followed by neutral sentiment (734 comments), and only a small number of positive sentiments (245 comments). The performance of classical Machine Learning (ML) models was severely degraded due to this imbalance, particularly in detecting the minority class. This study applied TF-IDF weighting for feature extraction, followed by the Synthetic Minority Oversampling Technique (SMOTE) to balance the training data (1,927 samples) before comparing the performance of three ML algorithms: Logistic Regression, Support Vector Machine (SVM), and LightGBM. The evaluation results on the testing dataset (482 samples) demonstrate that the implementation of SMOTE significantly improved the models’ ability to recognize the “Positive” class. The LightGBM model combined with SMOTE achieved the best performance, with an accuracy of 64.11%. In particular, the application of SMOTE successfully increased the minority-class F1-score from a baseline of 18.18% to 35.29%. These findings confirm that handling imbalanced data is a critical step in producing valid and reliable sentiment analysis results.
Comparison of Machine Learning Algorithms for Credit Score-based Banking Customer Churn Prediction Suryadillah Hendrawinata; Jasmir Jasmir; Gunardi Gunardi
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6148

Abstract

A high customer churn rate represents a significant challenge for the banking industry, leading to substantial financial losses and higher acquisition costs for new customers. Proactively identifying customers who are likely to churn is essential for implementing effective retention strategies. This study aims to address this issue by implementing and comprehensively comparing three different machine learning classification algorithms: Logistic Regression, Random Forest, and XGBoost. The study utilized a secondary dataset consisting of bank customer profiles from 10,000 customers with various characteristics, including credit scores, account balances, and transaction activities. The research methodology followed the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The models were evaluated using several metrics, including Accuracy, Precision, Recall, F1-Score, and ROC-AUC. The findings indicate that the ensemble models significantly outperformed the linear model (Logistic Regression), which achieved an F1-Score of only 0.286. Random Forest emerged as the best-performing model in this study, achieving the highest Accuracy (0.864), F1-Score (0.590), and ROC-AUC (0.852). In comparison, XGBoost demonstrated competitive performance with an F1-Score of 0.579 and a ROC-AUC of 0.832. The study concludes that Random Forest provides the most optimal overall performance, offering the strongest capability for identifying at-risk customers within the dataset.
Perancangan Sistem Informasi Pengarsipan Digital Data Pelanggan pada PT. Jambi Independent Press Berbasis Web Miranda Miranda; Ronald Naibaho; Gunardi Gunardi
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 4 No. 3 (2026): Juli : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v4i3.923

Abstract

PT. Jambi Independent Press is one of the companies engaged in publishing newspapers in its data processing using Microsoft Excel, but there are still many obstacles in data processing, such as the difficulty of recording customer data archiving data, planning previously planned activities because the data search process is valued slow, data does not appear automatically so you have to input it repeatedly, and data cannot be integrated with each other because there is no database. The purpose of this study is to analyze the system that is currently running, in order to overcome the problems faced at PT. Jambi Independent Press, by designing a Customer Data Digital Archiving Information System Design at PT. Web-Based Jambi Independent Press. The research framework that will be carried out in solving the problems discussed is identifying, conducting information searches based on theoretical foundations, collecting data using observation and interview methods, analyzing to find solutions to problems faced by PT. Jambi Independent Press. The system development method uses the waterfall model, the implementation of this research uses the PHP Programming Language and MySQL DBMS, to produce data processing applications that are expected to facilitate data processing and report generation.
Peran Regulasi Nasional dan Perjanjian Bilateral Dalam Penyelesaian Sengketa Menurut Hukum Bisnis di Indonesia Dwi Sukma Ramdhani; Gunardi Gunardi
Jurnal Kajian Hukum Dan Kebijakan Publik | E-ISSN : 3031-8882 Vol. 3 No. 1 (2025): Juli - Agustus
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62379/qc173786

Abstract

The development of international business must be based on clear legal certainty for investors and business actors in Indonesia. National regulations form the main legal basis, while bilateral agreements provide additional protection for foreign investors. This study aims to analyze the interaction between the national legal framework and international commitments, particularly in the context of bilateral investment agreements. The approach used is normative juridical with literature study and document analysis. The results of the study show that national regulations provide domestic legal certainty, while bilateral agreements strengthen international cooperation and facilitate foreign investment. However, potential conflicts may arise when national regulations are not fully aligned with international agreements, requiring careful legal harmonization. In conclusion, Indonesia needs to strengthen its regulatory framework to be compatible with bilateral agreements in order to create a conducive and competitive business climate..
Co-Authors Abdussalaam, Falaah Ahmad Husaein Annisa, Tiko Nur Arvita, Yulia Asyhadi, Ahmad Ayu Feranika Bambang Sukoco Bayu Perdana Calista, Stephanie Dwi Sukma Ramdhani Eddy Suratno Effiyaldi, Effiyaldi Fadilatul Muhamad Feranika, Ayu Ghea Permata Rizky Gunardi Husaein, Ahmad Jasmir , Jasmir Jasmir Jasmir Jasmir, Jasmir Johni Paul Karolus Pasaribu Joni Devitra Kasrizal Kasrizal Kristeria, Kris MARDIANA Maria Rosario Borroek Melani Kadar Miranda Miranda NAIBAHO, RONALD Novita, Dwi Pareza Alam Jusia, Pareza Alam Pratomo, Dwi Joko Purnama, Benni Rajhu Ilham Pradana Riza Pahlevi, Riza Rohaini, Eni Salsa Della Anggraini Selfi Kumara Hati Sharipuddin, Sharipuddin Sherin Devanty Situmorang Sukipardika, Abel Suryadillah Hendrawinata Vira Aryati Aryati William Zaenal Abidin, Dodo