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All Journal Semantik Techno.Com: Jurnal Teknologi Informasi JURNAL DIAN MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Journal of Information System E-Dimas: Jurnal Pengabdian kepada Masyarakat RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Pilar Nusa Mandiri JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT
MY. Teguh Sulistyono
Universitas Dian Nuswantoro
Computer Science & IT Economics, Econometrics & Finance Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences Other

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ANALISIS TINGKAT PELANGGARAN LALU LINTAS DI WILAYAH POLRES GROBOGAN MENGGUNAKAN METODE CLUSTERING K-MEANS Niken Silvia Anggraini; MY Teguh Sulistyono
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6834

Abstract

The increasing number of vehicle users in Grobogan Regency has resulted in a high number of traffic violations that have the potential to cause accidents and traffic congestion. This study aims to analyze the level of traffic violations using the K-Means clustering method. The data used is primary data from the Grobogan Police Traffic Unit for the 2020-2024 period with a total of 38,318 violation cases. The research process consists of data preprocessing which includes number extraction, text transformation, and normalization, determining the number of clusters using the Elbow method, and a clustering evaluation process using the Davies-Bouldin Index (DBI). The results of the analysis show that the data on the level of traffic violations can be grouped into minor violations of more than 3,000 cases, which include violations of not wearing a helmet and violating traffic signs, moderate violations of approximately 5,000 cases, which include violations of not carrying a driver's license or vehicle registration, and severe violations of approximately 1,000 cases, which involve large vehicles. Evaluation of the quality of clustering using the Davies-Bouldin Index (DBI) shows that the arrangement with three groups (k = 3) provides the smallest DBI value, namely 0.486770. This figure indicates that the formation of three groups is the best arrangement, so that the results of the grouping divided into categories of minor, moderate, and severe violations have a fairly good quality of grouping. These results are expected to be a reference for the Regional Police and local governments in developing more effective law enforcement strategies and traffic safety policies.
KLASIFIKASI TINGKAT PELANGGARAN LALU LINTAS DI WILAYAH GROBOGAN DENGAN PERBANDINGAN ANTARA METODE KNN DAN SVM Dea Adwitiya Daffa; MY Teguh Sulistyono
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6854

Abstract

The increasing number of motorized vehicle users in Grobogan Regency has resulted in an increasing number of traffic violations that have the potential to cause accidents. Each violation has a different level of severity, so a classification method is needed to assist in determining the appropriate action. This study aims to compare the performance of the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) methods in classifying the severity of traffic violations into three categories: minor, moderate, and severe. The research data was obtained from the Grobogan Police Department in 2020–2024 with a total of 38,318 cases. The research stages include data preprocessing, transformation, labeling using the K-Means method, data division, feature selection with Chi-Square, and classification using KNN and SVM. The evaluation results show that the KNN method produces an accuracy of 87.7% with an F1-score of 84.8%, but the AUC (64.3%) and MCC (21.1%) values ​​are still low, making it less than optimal in distinguishing classes. Meanwhile, the SVM method excelled in all evaluation metrics, with accuracy, precision, recall, F1-score, and MCC values ​​each reaching 98.5%. The distribution of classification results showed that KNN tended toward the mild class, while SVM was able to separate the three classes more evenly. Thus, SVM proved more effective in classifying traffic violation severity in Grobogan, producing more accurate results.
ANALISIS SENTIMEN KEBIJAKAN PEMBLOKIRAN REKENING PPATK DI MEDIA SOSIAL X MENGGUNAKAN TF-IDF, SMOTE SERTA PERBANDINGAN SVM DAN DECISION TREE David Wahyu Setyo Aji; Asih Rohmani; MY Teguh Sulistyono
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6877

Abstract

This study aims to analyze public sentiment toward the policy of the Pusat Pelaporan dan Analisis Transaksi Keuangan (PPATK) regarding bank account blocking, as expressed on the social media platform X (formerly Twitter), using a machine learning approach. The research data were collected through a web crawling process that gathered 799 tweets using Tweet Harvest during the period of July 28 to August 1, 2025. This time frame was selected because discussions related to account blocking by PPATK experienced a significant surge and became one of the viral topics on social media X. The analytical stages consisted of text preprocessing, data visualization, sentiment labeling using VADER, feature extraction with Term Frequency–Inverse Document Frequency (TF-IDF), data splitting, class balancing using the Synthetic Minority Oversampling Technique (SMOTE), and the development of classification models employing Support Vector Machine (SVM) and Decision Tree (DT) algorithms. The results indicate that the majority of public opinion was negative, accounting for 92.1% of the data, while positive opinions comprised only 7.9%. Both SVM and DT achieved high accuracy levels ranging from 93% to 94%. However, after hyperparameter tuning using GridSearchCV, the Decision Tree model demonstrated more balanced performance in detecting the minority (positive) class, with a precision of 0.73, recall of 0.65, and an F1-score of 0.69. These findings suggest that the integration of comprehensive preprocessing, TF-IDF feature representation, SMOTE-based class balancing, and parameter tuning can significantly enhance sentiment classification performance. Furthermore, this study provides a comprehensive overview of public perceptions of government policy. The results highlight the importance of utilizing machine learning–based sentiment analysis as a strategic consideration in formulating public policies that are more responsive to societal aspirations.
PERBANDINGAN AKURASI MODEL ARIMA DAN PROPHET DALAM MEMPREDIKSI HARGA SAHAM PT INDOFOOD SUKSES MAKMUR Yanuar Anggara Firdaus; MY Teguh Sulistyono
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6878

Abstract

Stock price predictions are an important aspect in supporting investment decision-making, especially for large companies whose stock prices fluctuate dynamically. This study aims to compare the accuracy levels of the AutoRegressive Integrated Moving Average (ARIMA) and Prophet models in predicting the closing price of PT Indofood Sukses Makmur Tbk (INDF.JK) shares. The historical data used covers the period from January 1, 2019, to April 28, 2025, and was obtained from web scraping yahoo.finance.com. The research stages include data preprocessing, stationarity testing, training and testing data division, ARIMA and Prophet modeling, and performance evaluation using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) metrics. The results show that the Prophet model with the parameter changepoint_prior_scale = 0.01 provides higher accuracy with an RMSE of 499.76, an MAE of 424.41, and an MAPE of 6.46% compared to ARIMA(1,1,0), which produces an RMSE of 907.50, an MAE of 701.36, and an MAPE of 9.98%. This study shows that Prophet produces better prediction performance for INDF.JK stocks, making it an alternative stock price forecasting model with an acceptable level of relative error.  
HYBRID WEIGHTED RANDOMIZATION UNTUK DISTRIBUSI PELANGGAN ADIL PADA WEBSITE DEALER OTOMOTIF Alvino Radyo Danisworo; Heru Lestiawan; MY Teguh Sulistyono
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6935

Abstract

Automotive dealers with large sales teams face challenges in fairly distributing customer prospects. Round-robin methods ignore performance differences, while performance-based allocation causes overload and team demotivation. This study develops a Hybrid Weighted Randomization algorithm integrating fairness score (40%), performance score (40%), time decay (20%), and penalty factor. Design Science Research methodology is employed with evaluation through 9 factorial configurations (5, 10, 20 agents; 100, 500, 1000 assignments) measuring Jain's Fairness Index and Coefficient of Variation. The system is implemented using Python 3.13 with PostgreSQL. Results show 88.9% of configurations achieve Grade A+ (JFI ≥ 0.95). Optimal configuration: 10 agents @ 1000 assignments with JFI=0.9916 (0.84% gap from perfect fairness). Three empirical scaling laws are identified: (1) minimum 25 assignments per agent for Grade A+, (2) optimal agent count ≈ √(N_total/10), (3) improvement rate ∝ 1/n_sales². Linear time complexity O(n·m) with 257ms per assignment confirms production-readiness. The algorithm successfully achieves near-optimal fairness while accommodating performance recognition and temporal factors. Scaling laws provide actionable frameworks for team sizing and capacity planning, with theoretical contributions (novel algorithm) and practical ones (production-ready implementation with clear deployment guidelines).
ASSOCIATION RULE MINING PADA POLA PEMBELIAN FURNITURE ONLINE STUDI KASUS DATASET SPARSE TAHUN 2025: ASSOCIATION RULE MINING–BASED ANALYSIS OF ONLINE FURNITURE PURCHASING PATTERNS: A CASE STUDY USING THE 2025 SPARSE DATASET Selvi Wijayanti; MY Teguh Sulistyono
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7575

Abstract

The rapid growth of e-commerce has driven an increase in household product transactions, including furniture, resulting in large-scale and diverse transactional data. However, most furniture transaction datasets exhibit sparse characteristics, as each order typically contains only one or a small number of items, making it difficult to generate frequent itemset combinations in association rule analysis. This study applies the Apriori algorithm to analyze online furniture sales transaction data from 2025, consisting of 1,938 records and 14 attributes. The research stages include data cleaning, transaction transformation, one-hot encoding, and the determination of a minimum support threshold of 0.0005 and a minimum confidence threshold of 0.1, adjusted to the characteristics of the dataset. The results indicate the formation of 421 frequent itemsets that meet the specified criteria, with a dominance of single-item itemsets caused by the low variation of items within each transaction. The analysis further shows that the generation of association rules is highly limited and fails to produce meaningful product relationship patterns. This limitation is primarily attributed to the highly sparse nature of the dataset, with an average of one item per transaction. This study demonstrates that applying the Apriori algorithm to highly sparse datasets results in very limited patterns and is dominated by single-item itemsets. These findings serve as an empirical study and a cautionary tale regarding the limitations of the Apriori algorithm when applied to transaction data with an average of one item per transaction, particularly in generating longer itemsets. Overall, this research contributes as an empirical warning on the limitations of applying the Apriori algorithm to highly sparse transaction datasets and emphasizes the importance of analyzing data characteristics prior to implementing association rule mining on e-commerce platforms.  
PREDIKSI KONSUMSI HARIAN PROGRAM MAKAN BERGIZI Iklima Madina Rachmawati; Aris Nurhindarto; MY Teguh Sulistyono
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7609

Abstract

The Nutritious Meal Program (MBG) aims to improve nutritional status and learning concentration among elementary school students; however, actual food consumption remains a critical challenge. This study develops a predictive model of daily MBG consumption based on students’ satisfaction and perceptions, and compares the performance of Random Forest and XGBoost algorithms. Data were collected from 132 students using a 10-item Likert-scale questionnaire and direct observation of plate waste. The target variable, “daily consumption,” was operationalized as an ordinal variable with three categories: 0 (rarely), 1 (sometimes), and 2 (often). Data were split using stratified sampling (80:20) and optimized through Grid Search with 5-fold cross-validation. The results indicate that XGBoost outperforms Random Forest, achieving lower MAE, MSE, and RMSE values, with statistically significant differences (p < 0.05). Feature importance analysis reveals that willingness to continue the program, perceived cleanliness, perceived taste, and grade level are the most influential factors affecting consumption. These findings highlight the critical role of students’ perceptions and attitudes in program effectiveness and demonstrate the potential of predictive modeling to support data-driven decision-making in school nutrition management.  
UI/UX SISTEM INFORMASI PRODUKSI HARIAN BERBASIS WEB MENGGUNAKAN METODE PROTOTYPE PADA PT DAW SON INDONESIA Dewi Eldiana Zahra; Aris Nurhindarto; Indra Gamayanto; MY Teguh Sulistyono
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.v11i2.8226

Abstract

Daily production data recording at PT Daw Son Indonesia is currently carried out using paper-based media and manual spreadsheets, leading to information delays, human error risks, and a lack of real-time monitoring. This study aims to design and develop a web-based, integrated production reporting information system to support tactical management decision-making. The system development explicitly utilized the Prototype method, emphasizing an iterative cycle with users to ensure functional alignment. The application was developed using the PHP programming language and a MySQL database. Functional testing was performed using the Black Box Testing method in a standardized environment. The test results demonstrated a system success rate of 94.44% (17 out of 18 scenarios met), where role-based authentication modules, production data management (CRUD), automated achievement calculations, and security protection against SQL Injection and XSS vulnerabilities functioned optimally according to specifications. Overall, this information system is declared highly feasible for implementation to enhance operational efficiency, guarantee daily data accuracy, and accelerate manufacturing trend monitoring at PT Daw Son Indonesia.
Segmentasi Data Sinyal EEG Berdasarkan Domain Waktu Sebagai Dasar Dalam Pengolahan Sinyal Pengambilan Keputusan Dalam Rehabilitasi Stroke MY Teguh Sulistyono; Dyah Ernawati; Stalina Anggraeny Dewi Amodia; Davin Hernanda Putra
JOINS (Journal of Information System) Vol 9 No 1 (2024): Edisi Mei 2024
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v9i1.9561

Abstract

Penyakit stroke adalah salah satu penyakit kardiofaskuler  jika menyerang akan menyebabkan cacad permanen dan meninggal dunia. Proses pemeriksaan membutuhkan dokter hanya berdasarkan visual unruk mendiagnosa penyakit, jika pemeriksaan dilakukan banyak dokter maka diagnose akan berbeda-beda. Unruk menghindari hal tersebut dibutuhkan alat EEG untuk mengambil aktivitas gelombang otak yang hasil pengambilan data tersebut dalam bentuk data mentah. Data mentah agar dapat dihasilkan untuk proses analisis diperlukan pemrosesan sinyal yang terdiri dari band pass filter, cleaning data, segmentasi dan decomposisi. Permasalahan selama ini yang timbul bahwa data mentah tersebt masih dalam bentuk data yang masih banyak noise baik dari pergerakan mata ataupun aktivitas otot., sehingga data mentah yang telah diolah akan menjadi dasar dalam pemilihan feature. Penelitian ini menggunakan metode penelitian menggunakan 2 tahapan yaitu data mentah dan pre processing, dimana pre processing memiliki 3 langkah yaitu band pass filter, cleaning data dan segmentasi.Hasil akhir dari penelitian ini adalah Segmentasi Data Sinyal EEG Berdasarkan Domain Waktu Sebagai Dasar Dalam Pengolahan Sinyal Pengambilan Keputusan Dalam  Rehabilitasi Stroke. Kata kunci: EEG, Stroke, Segmentasi, Cleaning Data, Band Pass Filter, Feature
Co-Authors Achmad Teguh Wibowo Adi, Derry Pramono Agung Widodo Agus Hermanto Agustinus Bimo Gumelar Aisyiyah Tri Ratna Dewi Alvino Radyo Danisworo Alzami, Farrikh Amartya Anugrah Indrawati Lestari Annabil, Muhammad Naufal Arunia, Aurelya Prameswari Asih Rohmani Asih Rohmani, Asih Azzami, Salman Yuris Adila Belinda Elsa Aulia Sinatrya Candra Irawan David Wahyu Setyo Aji Davin Hernanda Putra Dea Adwitiya Daffa Dewi Eldiana Zahra Dyah Ernawati Fahmi Amiq Hadi, Heru Pramono Heru Lestiawan Ika Novita Dewi Iklima Madina Rachmawati Indra Gamayanto Junaedi, Lukman Kurnia Desita, Raafi Maulana Zaky Muhammad MG Catur Yuantari Niken Silvia Anggraini Nugroho, Via Ardianto Nur Cahyo, Sefrico Aji Nur Sela Ameiliawati Nurhindarto, Aris Nurhindarto, Aris Prasetya, Rakan Shafy Ramadhan Rakhmat Sani Richard Emmerig S. Hadiati Nugraini Selvi Wijayanti Siti Hadiati Nugraini Stalina Anggraeny Dewi Amodia Sudaryanto Sudaryanto Sudaryanto Sudaryanto Wahibullah, Wahibullah Wellia Shinta Sari Yanuar Anggara Firdaus