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All Journal Jupiter EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Scientific Journal of Informatics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi ARSY : Jurnal Aplikasi Riset kepada Masyarakat Journal of Social Research Jurnal Algoritma Jurnal Informatika dan Kesehatan INOVTEK Polbeng - Seri Informatika
Agung Wibowo
Universitas Ngudi Waluyo
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Medicine & Pharmacology Public Health Social Sciences

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Implementasi Machine Learning untuk Prediksi Performa Lari Berdasarkan Data Strava Ardi Kurniawan; Didiet Hendrawan; Agung Wibowo
Jurnal Informatika dan Kesehatan Vol. 3 No. 1 (2026): IKN : Jurnal Informatika dan Kesehatan
Publisher : Universitas Ngudi Waluyo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35473/ikn.v3i1.4870

Abstract

This study aims to develop a predictive model to estimate running performance represented by pace (min/km) using activity data from Strava. The motivation stems from the fact that runners’ daily activity logs are often used only for descriptive tracking rather than as an evidence-based foundation for personalized and predictive training planning. The dataset consists of 120 running activities, with predictors including distance (km), training duration (min), elevation gain (m), and heart rate (bpm). Data preprocessing involved invalid record removal, outlier handling, and format standardization. A multiple linear regression model was then constructed and evaluated using the coefficient of determination (R²) and error metrics, namely Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) on the test set. The results indicate that training load and physiological variables jointly explain a meaningful proportion of pace variability, offering a quantitative basis for understanding factors associated with running performance. Overall, these findings suggest that Strava data can be leveraged to build practical performance prediction models to support data-driven training decisions.   ABSTRAK Penelitian ini bertujuan mengembangkan model prediktif untuk memperkirakan performa lari yang direpresentasikan oleh pace (menit/km) menggunakan data aktivitas dari platform Strava. Permasalahan yang melatarbelakangi penelitian ini adalah pemanfaatan data aktivitas harian pelari yang masih dominan bersifat deskriptif (evaluasi masa lalu) dan belum banyak digunakan untuk mendukung perencanaan latihan yang lebih terukur dan personal. Dataset penelitian terdiri dari 120 aktivitas lari, dengan variabel prediktor meliputi jarak tempuh (km), durasi latihan (menit), perubahan elevasi (m), dan denyut jantung (bpm). Data dipraproses melalui pembersihan data tidak valid, penanganan nilai ekstrem, dan standarisasi format, kemudian dianalisis menggunakan regresi linear berganda. Evaluasi model dilakukan menggunakan koefisien determinasi (R²) serta metrik galat Mean Absolute Error (MAE) dan Root Mean Square Error (RMSE) pada data uji. Hasil penelitian menunjukkan bahwa kombinasi variabel latihan dan fisiologis dapat menjelaskan variasi pace secara bermakna, serta memberikan dasar kuantitatif untuk memahami faktor-faktor yang berasosiasi dengan performa lari. Temuan ini mengindikasikan bahwa data Strava berpotensi dimanfaatkan untuk membangun model prediksi performa yang aplikatif sebagai dukungan pengambilan keputusan latihan berbasis data.
Implementasi dan Evaluasi Sistem Informasi Manajemen Keuangan UMKM Berbasis Digital di Kabupaten Semarang Menggunakan Pendekatan Design Science Research Kustiyono; Agung Wibowo; Ajeng Ramadhanti Syafitri
Jurnal Informatika dan Kesehatan Vol. 3 No. 1 (2026): IKN : Jurnal Informatika dan Kesehatan
Publisher : Universitas Ngudi Waluyo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35473/ikn.v3i1.4993

Abstract

Micro, Small, and Medium Enterprises (MSMEs) in Kabupaten Semarang play a strategic role in the regional economy; however, they still face challenges in transaction recording and financial statement preparation. Many MSMEs have not implemented a structured financial management information system, resulting in financial information that is neither accurate nor timely. This study aims to implement and evaluate a digital-based accounting management information system for MSMEs developed using the Design Science Research (DSR) approach. The DSR method was applied through the stages of problem identification, definition of solution objectives, design and development, demonstration, and evaluation. The results indicate that the implemented financial management information system improves the accuracy of financial record-keeping, enhances the efficiency of financial report preparation, and supports managerial decision-making within MSMEs. This study provides practical contributions for MSMEs in Kabupaten Semarang and academic contributions to the development of financial management information system research based on the DSR approach.   ABSTRAK Usaha Mikro, Kecil, dan Menengah (UMKM) di Kabupaten Semarang memiliki peran strategis dalam perekonomian daerah, namun masih menghadapi permasalahan dalam pencatatan transaksi dan penyusunan laporan keuangan. Banyak UMKM belum menerapkan sistem informasi manajemen keuangan yang terstruktur sehingga  informasi manajemen keuangan tidak tersedia secara akurat dan tepat waktu. Penelitian ini bertujuan untuk mengimplementasikan dan mengevaluasi sistem informasi manajemen akuntansi UMKM berbasis digital yang telah dikembangkan dengan menggunakan pendekatan Design Science Research (DSR). Metode DSR diterapkan melalui tahapan identifikasi masalah, penentuan tujuan solusi, perancangan dan pengembangan, ujicoba, serta evaluasi. Hasil penelitian menunjukkan bahwa sistem informasi manajemen keuangan yang diimplementasikan mampu meningkatkan akurasi pencatatan keuangan, efisiensi penyusunan laporan, serta mendukung pengambilan keputusan manajerial UMKM. Penelitian ini memberikan kontribusi praktis bagi UMKM di Kabupaten Semarang dan kontribusi akademik dalam pengembangan penelitian sistem informasi manajemen keuangan berbasis DSR.
Analisis Sentimen Ulasan Produk Marketplace Indonesia Menggunakan Naive Bayes dan SVM dengan Label Berdasarkan Rating Levi Ardin Gulo; Agung Wibowo
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3206

Abstract

Sentiment analysis aims to identify user opinions about products on marketplaces such as Shopee and Tokopedia. This study classifies product review sentiment using Naive Bayes (NB) and Support Vector Machine (SVM). The dataset underwent text preprocessing, including case folding, tokenization, stopword removal, and stemming, then was represented using TF-IDF. The results show that Support Vector Machine (SVM) achieved the highest accuracy of 94.54%, but had a very low negative class recall (5.71%), indicating a strong bias towards the majority class. In contrast, Naïve Bayes (NB) recorded a lower accuracy of 67.88%, but showed more balanced performance with a negative class recall of 48.57%. Conversely, NB provided more balanced performance between positive and negative classes despite its slightly lower accuracy. These findings emphasize the importance of considering class imbalance in sentiment analysis, especially for applications that require consumer complaint detection. This research is expected to serve as a reference for the development of automatic sentiment analysis systems on marketplace platforms with a focus on performance balance between classes.
Analisis dan Implementasi Sistem Pencatatan Produksi Berbasis Web dengan Modul Prediksi Menggunakan Metode ARIMA Dhamar Dhuha; Agung Wibowo
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3242

Abstract

Small and Medium Enterprises (SMEs) still widely use manual production recording, which is prone to errors and does not support rapid performance analysis. This study developed a digital production recording system integrated with forecasting features to support data-driven decision making. The system was built using the Laravel framework and applied the Autoregressive Integrated Moving Average (ARIMA) algorithm to predict production volumes based on historical data from September 2024 to September 2025, with a one-month forecasting horizon. The evaluation was conducted by comparing the prediction results with actual production data. The test results showed that the ARIMA model performed well, with a Mean Absolute Error (MAE) of 21.6 and a Mean Absolute Percentage Error (MAPE) of 1.99%, indicating a low level of prediction error. The integration between the digital recording system and the forecasting model allows production managers to monitor production history in a structured manner, obtain estimates of production for the next period, and accelerate the preparation of operational reports. The scientific contribution of this research lies in the development of a system framework that combines automated recording and predictive analytics in an integrated manner, thus offering a new approach to improving operational efficiency and data-driven decision making in the SME sector.
Perbandingan K-Medoids(PAM) Dan K-Means Untuk Semgentasi Produk Smartphone Di Shoope Indonesia Berdasarkan Harga Rating Dan Jumlah Ulasan: Studi Periode Maret 2026 Amanda Nur Haliza; Agung Wibowo
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3324

Abstract

This study aims to segment smartphone products based on the variables of Price, Rating, and Number of Reviews using the K-Means and K-Medoids methods. The dataset used consists of 400 smartphone products that have undergone data normalization as part of the preprocessing stage. The number of clusters was determined using an internal evaluation method, and the optimal number of clusters was found to be four (K=4). The clustering results show that both methods are capable of forming significantly different product group characteristics based on a combination of price level, user rating quality, and review intensity. The K-Means method produces a more structured cluster separation based on centroid values and is effective in representing the average data distribution. Meanwhile, K-Medoids demonstrate better resilience against outliers because cluster centers are represented by actual objects (medoids), making them more stable on heterogeneous data. Based on a comparative analysis of the methods’ characteristics and cluster evaluation results, K-Medoids demonstrates more robust performance for datasets with significant price variation. The findings of this study can serve as a basis for decision-making in marketing strategies and product clustering on e-commerce platforms.
Kombinasi Decision Tree dan Naïve Bayes dengan Explainable AI untuk Prediksi Dropout Agung Wibowo; Kustiyono; Eko Nur Hermansyah
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3367

Abstract

Predicting student dropout risk is crucial for supporting early intervention and accountable academic decision-making. This study proposes a multi-class classification (Dropout, Enrolled, Graduate) using voting (Naïve Bayes and Decision Tree) and Explainable AI to enhance transparency. The dataset consists of 4,424 records with 36 features. Evaluation was conducted using k-fold stratified cross-validation (k=10) and the F1-macro metric. The results show that model performance is relatively close and stable at k=10, so model selection must consider the trade-off between performance and interpretability. The main contribution of this research is a web-based early warning DSS prototype that integrates Voting (NB+DT) with an XAI module (SHAP–LIME) so that predictions can be explained, audited, and followed up with academic intervention recommendations.
Digital Transformation Acceleration and Character-Based Excellent Human Resources Capacity in Fostering Sustainable Rural Economy Based on Tourism and Islamic Economics: A SLR Mufti Agung Wibowo; Setya Indah; Abdul Aziz; Ari Siswati; Agung Wibowo
Journal of Social Research Vol. 5 No. 8 (2026): Journal of Social Research
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/josr.v5i8.3294

Abstract

This study examined the acceleration of rural economic transformation through the integration of digital technology, human resource capacity, sustainable tourism, and Islamic economic principles. The background of this research was rooted in the persistent development gap between urban and rural areas, particularly in digital access, managerial capacity, and sustainable economic structures within village-based enterprises. The objective of this study was to synthesize existing literature to develop a comprehensive understanding of how digital transformation and character-based human resource capacity contribute to sustainable rural economic development within tourism and sharia-based frameworks. The method used was a Systematic Literature Review (SLR) following the PRISMA 2020 protocol. Data were collected from reputable databases such as Scopus, Web of Science, and Google Scholar, covering studies published between 2010 and 2025. A total of 100 selected articles were analyzed using thematic synthesis to identify patterns, relationships, and conceptual developments. The results showed that digital transformation significantly improved rural economic efficiency, particularly through the digitalization of village-owned enterprises (BUMDes) and marketing innovation. However, its effectiveness strongly depended on human resource readiness, governance quality, and digital literacy. Furthermore, sustainable tourism and Islamic economic principles were found to enhance environmental protection, ethical governance, and inclusive economic distribution. In conclusion, rural development is most effective when digitalization, human capital development, and value-based economic systems are integrated holistically. This study contributes a synthesized conceptual framework for sustainable rural transformation and provides policy implications for strengthening digital ecosystems, human capacity, and sharia-compliant rural economic models.
Implementation of Agile and Waterfall Methods in a Web-Based Admission System for Streamlined Registration and Communication Wafiq Lana Pradana; Agung Wibowo
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/gj9qa035

Abstract

This research discusses the development of a web-based New Student Admission (PPDB) system using a hybrid approach of Agile and Waterfall. The Waterfall method is used for structured system planning and design, while Agile allows for iterations during development. The integration of these two methods ensures that the system is developed with good documentation and flexibility in testing and feature adjustments. This system aims to improve operational efficiency, data transparency, and ease of communication between prospective students and educational institutions. Based on the test results, the system is able to reduce data input errors by up to 30%, speed up the registration process by up to 50%, and increase user satisfaction by 85% based on surveys conducted. Additionally, communication features such as real-time notifications and registration status updates help to improve interaction between users and the school. With the combination of Agile and Waterfall, this system can adapt to the needs of educational institutions and ensure a more efficient and transparent student admission process.
Design of a Website-Based Employee Absence Information System Using Laravel at PT Hesed Indonesia Wimar Ardana Gulo; Agung Wibowo
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/ynjh9g19

Abstract

This study aims to design and implement a web-based employee attendance information system using the Laravel framework at PT Hesed Indonesia, with the main contribution being the integration of real-time attendance data management features, automatic reports, and centralized access rights settings that were not available in the previous system. This system was developed to replace the manual attendance method, which was prone to recording errors, data manipulation, and slowed down the reporting process. Development was carried out using the Waterfall method, which includes the stages of requirement definition, design, coding & testing, integration & system testing, and operation & maintenance. A trial was conducted on 25 employees over one month, resulting in an attendance recording accuracy rate of 98% and a 70% reduction in recapitulation time compared to the previous method. The implementation results show that the system can improve efficiency and ease of data access, although it still has limitations such as the lack of integration with fingerprint devices or payroll systems. In the future, this system can be developed with attendance notification features, IoT integration for automatic attendance, and attendance data analysis to support managerial decisions.
SMARTGRAD: Prediksi Kelulusan Tepat Waktu Mahasiswa Kampus Merdeka Agung Wibowo; Ade Pratama; Dwi Setiawan
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.v15i2.4605

Abstract

On-time graduation is a primary indicator of student success and serves as a key benchmark for the quality of higher education institutions. This study aims to develop SmartGrad, a prediction model for on-time graduation based on the Naive Bayes algorithm, supported by feature selection using Decision Tree. The model integrates academic variables (semester GPA, average grades) and non-academic variables (types of MBKM, employment status, age) to produce accurate and contextual predictions. The research dataset comprises 313 entries with 17 attributes, processed through feature selection and classification stages. Evaluation results demonstrate the model's excellent performance, with an average accuracy of 88.8%, precision of 90.5%, recall of 97.9%, and an F1-score of 94.0%. The implementation of SmartGrad as an interactive web application based on Streamlit supports transparent and easily comprehensible decision-making. The novelty of this research lies in the integration of MBKM factors and employment status into the prediction model, as well as the application of an interpretable AI approach to support higher education policies and the achievement of Sustainable Development Goal 4 (Quality Education). These findings are expected to serve as a strategic reference for higher education administrators in enhancing academic quality and the effectiveness of the Freedom of Learning Independent Campus program.
Co-Authors Abdul Aziz Abdul Rohman Ade Pratama Afif Muhamad, Armana Ajeng Ramadhanti Syafitri Ajeng Syafitri Amanda Nur Haliza Ameyriya Angraeni Ardi Kurniawan Ari Siswati Danny Manongga Dega Adhi Kusuma Desi Ramadani Putri Dhamar Dhuha Didiet Hendrawan Diva Riyandini Dwi Setiawan Ega Seladevi Eko Nur Hermansyah Farid Achmad Hindriyanto Dwi Purnomo Idham Renaldi Kustiyono Kustiyono Kustiyono Lailatul Fadhila Levi Ardin Gulo Mufti Agung Wibowo Retno Aoktaviani, Bunga Dea Ricky Darmawan Rohman, Abdul Setya Indah Ulya, Zahrotun Diyana Vani Jiwita Halawa Wafiq Lana Pradana Widia Jelita Gulo Wimar Ardana Gulo Yayi Suryo Prabandari Yoannes Romando Sipayung