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All Journal International Journal of Electrical and Computer Engineering TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Pro Bisnis Telematika Seminar Nasional Informatika (SEMNASIF) MIX : Jurnal Ilmiah Manajemen Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Matrix : Jurnal Manajemen Teknologi dan Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Building of Informatics, Technology and Science Jurnal Audience Journal of Information Systems and Informatics Journal of Innovation Information Technology and Application (JINITA) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Journal La Multiapp Decode: Jurnal Pendidikan Teknologi Informasi BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Pengabdian Mitra Masyarakat (JPMM) CSRID
Berlilana Berlilana
Universitas Amikom Purwokerto
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
DEVELOPMENT OF APPLICATION PROGRAMMING INTERFACE (API) FOR AMIKOM PURWOKERTO HANDSANITIZER (AMPUH) DATA LOGGER VISUALIZATION Agnis Nur Afa Zumaroh; Trisna Maulida; Hasri Akbar Awal Rozaq; Rona Sepri Ananda; Alif Yahya Syafaat; Imam Tahyudin; Berlilana
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 3 (2022): JUTIF Volume 3, Number 3, June 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.3.222

Abstract

The Internet of Things (IoT) of AMPUH adheres to three simple concepts: physical devices with IoT modules, internet-connected devices, and cloud data centers as data storage places. ThingSpeak is an IoT platform that is useful as a cloud-based data logger. Data loggers in the form of primary data or raw data need a web dashboard for data visualization because of not communicative. Therefore, this research has aim to construct API of AMPUH visualization for be used by frontend team. This research converted MATLAB programming language into PHP programming language. The data logger processing uses the powerful programming method because this method is time-efficient and can fix if an error occurs in the system development stage without having to repeat the process from the beginning. The Extreme Programming method has four steps: planning, design, coding, and testing. The processing of data loggers from the ThingSpeak platform uses the Laravel Framework to generate APIs that the frontend team will use. The researcher managed the data logger from ThingSpeak using the Laravel framework to produce several APIs used by the frontend team to visualize the data be interactive and informatively.
Comparative Analysis of DBSCAN, OPTICS, and Agglomerative Clustering Methods for Identifying Disease Distribution Patterns in Banjarnegara Community Health Centers Setiyawan, Dillyana Tugas; Berlilana, Berlilana; Barkah, Azhari Shouni
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4577

Abstract

The variation in disease distribution patterns across community health centers in Banjarnegara Regency necessitates a precise segmentation analysis to support effective allocation of healthcare resources. This study aims to compare the effectiveness of three clustering methods DBSCAN, OPTICS, and Agglomerative Clustering in grouping Puskesmas based on the type and number of diseases they manage. The evaluation methods used include the Silhouette Score and the Davies-Bouldin Index, which assess the quality of the clustering results. The analysis indicates that Agglomerative Clustering produces the most stable cluster structures, reflected in its highest Silhouette Score, compared to DBSCAN and OPTICS, which tend to yield more noise and less optimal clustering quality. These findings suggest that hierarchical clustering approaches are more effective in the context of healthcare service distribution data at the primary care level. The results of this study are expected to serve as a foundation for the formulation of data-driven and region-based health policies, particularly in designing more targeted interventions and optimizing the distribution of healthcare services.
Analysis of Technology Adoption Factors in Learning among Vocational Students using UTAUT2 Model Harimanto, Bambang; Berlilana, Berlilana; Barkah, Azhari Shouni
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4940

Abstract

Technology acceptance in vocational education is a key factor in supporting the effectiveness of teaching and learning processes in the digital era. This study aims to analyze the factors influencing technology acceptance among students of the Computer and Network Engineering (TKJ) Department at SMK Ma'arif 1 Kroya using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. The model includes the variables Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value, Habit, Behavioral Intention, and Actual Usage. The results reveal that five key variables—Performance Expectancy, Effort Expectancy, Social Influence, Hedonic Motivation, and Price Value—significantly influence Behavioral Intention, while Habit, Facilitating Conditions, and Behavioral Intention directly affect Actual Usage. All constructs in the model meet validity and reliability criteria, and no multicollinearity was detected (VIF < 3.3). The coefficient of determination (R²) values of 0.612 for Behavioral Intention and 0.673 for Actual Usage indicate strong predictive power of the model. These findings confirm the relevance of the UTAUT2 framework for understanding and enhancing technology acceptance in vocational education settings and provide valuable insights for improving technology integration in technical learning environments.
Comparative Analysis of Data Balancing Techniques for Machine Learning Classification on Imbalanced Student Perception Datasets Saekhu, Ahmad; Berlilana, Berlilana; Saputra, Dhanar Intan Surya
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4286

Abstract

Class imbalance is a common challenge in machine learning classification tasks, often leading to biased predictions toward the majority class. This study evaluates the effectiveness of various machine learning algorithms combined with advanced data balancing techniques in addressing class imbalance in a dataset collected from Class XI students of SMK Ma'arif 1 Kebumen. The dataset, comprising 300 instances and 36 features, includes textual attributes, demographic information, and sentiment labels categorized as Positive, Neutral, and Negative. Preprocessing steps included text cleaning, target encoding, handling missing data, and vectorization. Four sampling techniques—SMOTE, SMOTE + Tomek Links, ADASYN, and SMOTE + ENN—were applied to the training data to create balanced datasets. Nine machine learning algorithms, including CatBoost, Extra Trees, Random Forest, Gradient Boosting, and others, were evaluated using four train-test splits (60:40, 70:30, 80:20, and 90:10). Model performance was assessed using metrics such as accuracy, precision, recall, F1-score, and AUC- ROC. The results demonstrate that SMOTE + Tomek Links is the most effective balancing technique, achieving the highest accuracy when paired with ensemble algorithms like Extra Trees and Random Forest. CatBoost also delivered competitive performance, showcasing its adaptability in imbalanced scenarios. The 90:10 train-test split consistently yielded the best results, emphasizing the importance of adequate training data for model generalization. This study highlights the critical role of data balancing techniques and robust algorithms in optimizing classification performance for imbalanced datasets and provides a framework for future research in similar contexts.
Enhancing Clustering Performance through Benchmarking of Dimensionality Reduction Techniques on Educational Data Priyanto, Eko; Berlilana, Berlilana; Tahyudin, Imam
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4297

Abstract

This study evaluates the effectiveness of dimensionality reduction techniques in enhancing clustering performance using a tracer study dataset of 500 alumni from UMNU Kebumen, containing 58 variables. The objective was to identify the optimal combination of dimensionality reduction and clustering methods for uncovering patterns in alumni profiles, job search strategies, and employment outcomes. Principal Component Analysis (PCA), Non- Negative Matrix Factorization (NMF), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) were applied, followed by clustering using K-Means, DBSCAN, and Hierarchical Clustering. The findings revealed that NMF achieved the highest clustering quality, particularly with K- Means and Hierarchical Clustering, outperforming PCA. NMF also demonstrated superior compactness with a Calinski-Harabasz Index of 287.96, compared to 125.88 for PCA. While t-SNE and UMAP delivered competitive results, their computational times of 245.8 and 76.5 seconds, respectively, made them less practical for large datasets. The novelty of this study lies in its comprehensive evaluation of dimensionality reduction techniques and the integration of diverse clustering algorithms to assess their interplay. The results provide actionable insights, recommending NMF for accuracy-critical tasks and PCA for time-sensitive applications. Given the increasing volume of high-dimensional educational data, this study highlights the critical need for efficient clustering strategies to extract meaningful insights, ultimately supporting data-driven decision-making in education and workforce planning. Addressing these challenges is essential to optimizing institutional strategies, improving student employability, and enhancing workforce alignment with industry demands.
Co-Authors Agnis Nur Afa Zumaroh Alif Yahya Syafaat Andi Dwi Riyanto Anggi Zahriyatun Anisa Antonius Agung Hartono Ayuni, Ratih Azhari Shouni Barkah Bagaskoro, Galih Bagus Adhi Kusuma Bahari, Aris Rifki Setiya Dewi Fortuna Dhanar Intan Surya Saputra Dhanar Surya Saputra, Dhanar Surya Didit Suhartono Eko Priyanto El Emary, Ibrahiem M. M. Emha Taufiq Luthfi Erliyana Nurrahma Fandy Setyo Utomo Fiby Nur Afiana Fikri Ramadhan Giat Karyono Harimanto, Bambang Harimato, Bambang Hasri Akbar Awal Rozaq Hendra Marcos Imam Tahyudin Imam Tahyudin Ipo Novianto istiqomah istiqomah Khoirul Abbi Rokhman M. Suyanto Melia Dianingrum Min Tsai Lai Muhamad, Nurita Murtiyoso Murtiyoso Nanjar, Agi Nur Aini Nurdiyansa, Zhafran Afif Pambudi, Rahmat Prastyo, Priyo Agung Prayoga, Fandhi Dhuga Prianto, Slamet Endro Primandani Arsi Putri, Eka Ardiya Rahman Rosyidi Rinda Utami Rizki Wahyudi Rona Sepri Ananda Rujianto Eko Saputro Saekhu, Ahmad Santi Dwi Astuti Saputra, Alfin Nur Aziz Sarmini - Sarmini Setiyawan, Dillyana Tugas Seto Wahyu Widayat, Seto Wahyu Sumartono Sumartono Taqwa Hariguna Taqwa Hariguna Taqwa Hariguna Tri Astuti Tri Wahyuningsih Trisna Maulida Udianti, Asih Wahid, Arif Mu’amar Wenti Risma Damayanti Wiga Maulana Baihaqi Yuli Purwati Zanuar Rifai