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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Sains dan Teknologi Techno.Com: Jurnal Teknologi Informasi EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Telematika : Jurnal Informatika dan Teknologi Informasi Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Ilmiah KOMPUTASI Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF APPLIED INFORMATICS AND COMPUTING SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) EDUMATIC: Jurnal Pendidikan Informatika Jurnal SITECH : Sistem Informasi dan Teknologi Journal of Information Systems and Informatics Jurnal Aisyah : Jurnal Ilmu Kesehatan Abdimasku : Jurnal Pengabdian Masyarakat International Journal of Marine Engineering Innovation and Research Idealis : Indonesia Journal Information System Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Komunita: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Pengabdian Masyarakat Inovasi Indonesia
Soni Adiyono
Universitas Muria Kudus
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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ANALISIS SENTIMEN ULASAN GOOGLE MAPS UNTUK REKOMENDASI COFFEE SHOP DI KUDUS MENGGUNAKAN TF-IDF DAN LOGISTIC REGRESSION: SENTIMENT ANALYSIS OF GOOGLE MAPS REVIEWS FOR COFFEE SHOP RECOMMENDATIONS IN KUDUS USING TF-IDF AND LOGISTIC REGRESSION Diyas Aditya Adi Saputra; Noor Latifah; Soni Adiyono
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7952

Abstract

The rapid growth of the café industry in Kudus has given residents plenty of options for places to hang out. Customer reviews on Google Maps serve as a vital information source, as they contain customer opinions and satisfaction levels regarding a particular cafe. However, the sheer volume of review data makes manual analysis less effective. This study aims to analyze the sentiment of Google Maps reviews for 10 cafés in Kudus (2023–2025) using the TF-IDF method and a Logistic Regression algorithm based on K-Fold Cross-Validation. Research data was obtained through web scraping, comprising 3,393 Google Maps reviews. The research stages included data preprocessing (text normalization, tokenization, stopword removal), feature extraction using TF-IDF, splitting the data into 80% training and 20% testing sets, training the Logistic Regression model, and evaluating model performance using K-Fold Cross-Validation. The experimental results show that the TF-IDF-based Logistic Regression model is capable of classifying positive and negative reviews well, yielding an accuracy of approximately 86,68%, precision of 92,45%, recall of 91,71%, and an F1-score of 92,08%. This study is expected to help the public determine recommendations for the best coffee shops in Kudus based on objective customer opinions, as well as serve as a reference for business owners to improve service quality and customer satisfaction.
ANALISIS KOMPARATIF SISTEM ERP UNTUK USAHA KECIL MENENGAH (UKM) RETAIL MENGGUNAKAN METODE TOPSIS Abdul Azis Al Baehaqi; Supriyono; Soni Adiyono
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8023

Abstract

Retail SMEs in Indonesia face significant challenges in selecting the right Enterprise Resource Planning (ERP) system due to budget constraints, limited human resources, and lack of systematic evaluation guidance. This research develops a desktop-based decision support system using Python 3.12 with the Flet framework, implementing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to assist retail SMEs in interactively selecting optimal ERP. The research analyzes seven ERP alternatives (SAP Business One, Oracle NetSuite, PeopleSoft, webERP, Compiere, Odoo, and Accurate Online) using eight main criteria with 26 sub-criteria covering Cost, Functionality and Integration, Time and Availability, Usage and Support, Data Management, Reputation and Strategy Vendor, System Quality, and Scalability. Criteria weights are established referring to systematic literature review with System Quality (0.254) and Data Management (0.248) as highest priorities. Each alternative was assessed based on a review of official vendor documentation, verified review platforms, and relevant academic literature. Analysis results show Odoo ranks first (Ci* = 0.7668), followed by Accurate Online (Ci* = 0.6985), indicating the superiority of open-source and local solutions in cost, system quality, and flexibility for Indonesian retail SME context. The developed decision support system provides practical contribution for retail SMEs in strategic ERP selection decision-making while offering an adaptive evaluation framework for various industry contexts.
KOMPARASI KINERJA MACHINE LEARNING TEROPTIMASI SMOTE DAN PSO PADA KLASIFIKASI SENTIMEN ULASAN ROBLOX Amanda Diyas Setiyoadi; Yudie Irawan; Soni Adiyono
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8034

Abstract

The emergence of digital platforms like Roblox has led to an increase in the number of user reviews on the Google Play Store. These reviews contain important information regarding public perception, satisfaction levels, and user complaints about the app. However, the large volume of reviews and the unstructured nature of the text make manual analysis inefficient. Therefore, an automated solution in the form of machine learning-based sentiment classification is needed. This study was conducted to evaluate and compare the effectiveness of three machine learning algorithms, namely Logistic Regression, Support Vector Machine (SVM), and Random Forest, in classifying Roblox app review sentiment into three categories: positive, neutral, and negative. The research data consisted of 10,000 reviews collected through a crawling process from the Google Play Store. Synthetic Minority Oversampling Technique (SMOTE) was applied to address class imbalance, while Particle Swarm Optimization (PSO) was used to optimize model parameters. Experimental results show that Random Forest combined with SMOTE achieved the highest performance with an accuracy of 0.7219, a precision of 0.7241, a recall of 0.7219, an F1-score of 0.7228, and an AUC of 0.778. However, the accuracy of 72.19% is still a limitation for direct practical application, so further improvements are needed. This study also developed a Streamlit-based dashboard to monitor sentiment classification results in real-time. Based on these findings, the combination of Random Forest and SMOTE can be considered quite effective, although it still has limitations in the level of model accuracy.
IMPLEMENTASI HYBRID AHP-TOPSIS PADA SISTEM PENDUKUNG KEPUTUSAN EVALUASI PERFORMA PRAMUDI Pratiwi Cahyaningtiyas; Rhoedy Setiawan; Soni Adiyono
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8210

Abstract

Subjective bias, delayed data accumulation, and unfair bonus allocation are common issues resulting from the manual pramudi appraisal method at PT Samudra Jaya Transport. To resolve these challenges, this research develops a web-based Decision Support System (DSS) integrating the Analytical Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The AHP method is employed to establish priority weights for five core criteria: cargo, attendance, discipline, fuel consumption, and fleet maintenance. Concurrently, TOPSIS is implemented to rank 107 pramudi partitioned into three distinct categories: New Pramudi, Senior Pramudi, and Experienced Pramudi. The AHP evaluation yields a reliable Consistency Ratio (CR) of 0.0259. Furthermore, the TOPSIS analysis identifies the leading preference scores for each cluster, specifically PB-01 at 0.7909, PS-01 at 0.8691, and PSE-01 at 0.9308. Black-Box testing confirms that all core system features function correctly. Ultimately, this system ensures a data-centric evaluation process, eliminates bias, and delivers highly transparent monthly bonus recommendations.
Explainable XGBoost Early-Warning Framework for Academic Stress-Based Student Mental Health Risk Mapping Supriyono; Heru Noviyanto Firmansyah; Soni Adiyono
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7812

Abstract

Existing university mental health monitoring often depends on voluntary help-seeking or manual questionnaire interpretation, which may delay early support for students experiencing academic stress. This study proposes an explainable XGBoost-based early-warning framework for non-clinical mapping of student mental health risk from academic stress indicators. The single-site dataset comprised 1,002 anonymized student records from Universitas Muria Kudus. K-Means clustering was used to transform DASS-21 depression, anxiety, and stress scores into low, moderate-, and high-risk categories, while XGBoost predicted the cluster-derived labels using seven single-item academic stress indicators and engineered aggregate and interaction features. On a stratified hold-out testing set of 201 records, the model achieved weighted precision, recall, and F1-score values of 0.8907, 0.8905, and 0.8906, respectively, with class-level F1-scores of 0.9109 for low risk, 0.8900 for moderate risk, and 0.8713 for high risk. Additional ablation, clustering sensitivity, subgroup, threshold, and SHAP stability analyses were conducted to strengthen robustness and interpretability. The findings show that cumulative academic stress and interaction features involving parental expectations, exam anxiety, and learning-method adaptation were consistently influential predictors. The framework is intended to support early institutional prioritization and counseling referral, not clinical diagnosis. Generalization remains limited by the single-institution sample and the use of single-item academic stress indicators; therefore, local retraining and recalibration are required before institutional deployment, including implementation of the Streamlit prototype.
Pendampingan Implementasi Sistem Informasi Armada Truk Berbasis Web Febriana Permatasari; Soni Adiyono
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 6 No. 3 (2026): Mei 2026 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v6i3.1161

Abstract

Sektor transportasi memiliki peran strategis dalam perekonomian Indonesia, namun transparansi bagi hasil pendapatan antara perusahaan dan sopir masih menjadi permasalahan. Kegiatan pengabdian ini bertujuan meningkatkan transparansi melalui penerapan sistem informasi manajemen transportasi berbasis web pada PT Djanjikita Putra Mandiri. Sistem dirancang dengan pembagian hak akses, yaitu admin sebagai pengguna utama, direktur sebagai pihak monitoring, dan sopir sebagai pengguna sistem terbatas yang dapat mengakses jadwal, informasi muatan, memperbarui status pengiriman, serta melihat pendapatan. Metode yang digunakan meliputi tahap persiapan, perancangan, implementasi, pelatihan, dan evaluasi. Hasil menunjukkan peningkatan signifikan, ditandai dengan akurasi perhitungan mencapai 100% (zero error) serta efisiensi waktu laporan dari ±2 hari menjadi 30 menit. Selain itu, pemahaman dan kepercayaan pengguna terhadap sistem juga meningkat. Sistem ini terbukti mampu meningkatkan transparansi, efisiensi, dan akurasi dalam pengelolaan operasional.
Implementasi Absensi Sopir Berbasis Foto dan Geolokasi di PT Djanjikita Putra Mandiri Nava Azahra; Soni Adiyono
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 6 No. 3 (2026): Mei 2026 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v6i3.1163

Abstract

Pengelolaan absensi sopir di PT Djanjikita Putra Mandiri masih dilakukan secara manual melalui WhatsApp, sehingga menyebabkan rendahnya akurasi data, keterlambatan, dan keterbatasan transparansi pada lingkungan kerja dengan mobilitas tinggi. Pengabdian ini bertujuan mengimplementasikan sistem absensi berbasis web yang mengintegrasikan verifikasi foto real-time dan geolokasi untuk meminimalkan manipulasi serta meningkatkan efektivitas pemantauan. Metode meliputi pengembangan sistem menggunakan SDLC waterfall, sosialisasi, pelatihan, implementasi, serta evaluasi kuantitatif dan kualitatif. Analisis kuantitatif dilaksanakan dengan membandingkan hasil pre-test dan post-test menggunakan uji Wilcoxon Signed-Rank. Hasil menunjukkan adanya peningkatan yang bermakna pada efisiensi, akurasi, transparansi, dan kedisiplinan pengguna dengan nilai (Z = -5.38; p < 0.001). Sistem ini mendukung transformasi digital dan pengambilan keputusan berdasarkan data. 
Digitalisasi Pelaporan Nota Operasional melalui Implementasi Sistem Informasi Berbasis Web pada PT Telkom Akses Kudus Anita Rahmawati; Soni Adiyono
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 6 No. 3 (2026): Mei 2026 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v6i3.1186

Abstract

Pengabdian kepada masyarakat ini dilatarbelakangi oleh proses pelaporan nota operasional di Service Area PT Telkom Akses Kudus yang masih dilakukan secara manual menggunakan Microsoft Excel dan Telegram, sehingga kurang efisien, memerlukan waktu lama, dan berpotensi menimbulkan kesalahan pencatatan. Kegiatan ini bertujuan untuk mengimplementasikan sistem informasi berbasis web guna mengintegrasikan proses pencatatan, verifikasi, rekapitulasi, dan pembayaran nota operasional. Metode pelaksanaan meliputi analisis kebutuhan, perancangan sistem, implementasi, pengujian, dan pemeliharaan. Hasil kegiatan menunjukkan bahwa sistem dapat digunakan dengan baik oleh admin dan teknisi serta memperoleh tanggapan positif dari pengguna. Sistem membantu mempermudah pengelolaan data, pencarian eviden, verifikasi, dan pemantauan pembayaran. Dengan demikian, penerapan sistem berbasis web penting untuk meningkatkan efisiensi, ketepatan, dan transparansi pelaporan nota operasional.  
An Integrated Safety Stock and Net Promoter Score System for Inventory and Customer Loyalty Arya Putra Badruzzaman; Yudie Irawan; Soni Adiyono
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33557

Abstract

Manual and separate inventory management and customer loyalty monitoring often lead to information delays, record-keeping errors, low operational efficiency, and an unmonitored relationship between stock availability and customer perception. The aim of our research was to develop a web-based sales and loyalty information system that integrates Safety Stock and Net Promoter Score (NPS) methods into a single decision support framework. Our research is a study of system development using the Waterfall model, which includes the stages of requirements analysis, system design, implementation, testing, and maintenance, supported by use cases and activity diagrams. The findings of this study are an integrated system that is able to calculate minimum stock levels, safety stocks, risk of stock-outs, and display real-time NPS visualizations. The test results obtained through black box testing on system access, inventory processing, and NPS reporting show that all key functions are running well and to specification. The implications of this study suggest that the proposed system can improve inventory accuracy, reduce the risk of stock shortages, improve operational efficiency, and support objective, responsive, and sustainable managerial decision-making for small and medium-sized distributors through an integrated and reliable information system.
Analisis Churn Menggunakan Metode K-Means Clustering Berdasarkan Model LRFM Untuk Meningkatkan Retensi Pada Mahes Printing Bagus Joko Winarso; Diana Laily Fithri; Soni Adiyono
EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Vol 15, No 2 (2025): December
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/expert.v15i2.4584

Abstract

In the competitive digital era, customer retention has become a critical factor for business sustainability, particularly in the digital printing industry which faces intense competition. Mahes Printing, despite recording a high transaction volume, continues to experience low repurchase rates due to fragmented and manual management of customer data and transaction history. This study aims to implement churn analysis within a Sales Management Information System using a Customer Relationship Management (CRM) approach supported by the LRFM (Length, Recency, Frequency, Monetary) model and the K-Means clustering algorithm. The results indicate that customers can be effectively grouped into three main clusters representing low, medium, and high churn risk levels. This segmentation facilitates the identification of customers with high churn potential, characterized by low Recency and Frequency values, thereby providing strategic insights to support data-driven decision-making and the development of more targeted and effective customer retention strategies.
Co-Authors - Supriyono Abdul Azis Al Baehaqi Adi Wibowo Alamul Huda, Alamul Alversia, Yeshika Amanda Diyas Setiyoadi Anita Rahmawati Anteng Widodo Aris Puji Widodo Arya Putra Badruzzaman Bagus Joko Winarso Damar Aji Gurowo Dewi Masitoh Diana Laily Fithri Diana Laily Fithri Diana Laily Fithri Diyas Aditya Adi Saputra Eko Darmanto Eko Darmanto Eko Sediyono fatia, imelda annas Febriana Permatasari Fina Fakhriyah Hadiprawoto, Triana Rahajeng Hawari, Syafrie Muhammad Heru Noviyanto Firmansyah Hidayat, Hilmi Bayu Ira Iriyanty Iriyanty, Ira Kurniasari, Nita Kurniawan, Aldhi Ari Laily Fithri, Diana Latifah, Noor Lu&#039;lu&#039;il Laili Mochammad Imron Awalludin Muhammad Arifin Nanda Dea Anggi Maharani Nava Azahra Noor Latifah Nur Mahardika Pratiwi Cahyaningtiyas Pratomo Setiaji Prayogo, Adhie R Rhoedy Setiawan R. Rhoedy Setiawan Rahmat Gernowo Rizky Adisaputra Romadhon, Zainur Romadhon, Zainur Romy Aziz Risaldi Salum Ainayya Alfatikhah Sendi Novianto Sendi Novianto Setyawan, Moh Dodi Sholichatunnita Nita Siti Masfuah Supriyono Supriyono Supriyono Susanti, Nanik Syafiul Muzid Syafrie Muhammad Hawari Taufiq Hidayat Triana Rahajeng Hadiprawoto Yeshika Alversia Yudie Irawan