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Jurnal Komputer Terapan (JKT) Badan Penelitian dan Pengabdian kepada Masyarakat (BP2M) Politeknik Caltex Riau Jl. UmbanSari No. 1 Rumbai - Pekanbaru 28265
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Jurnal Komputer Terapan
Published by Politeknik Caltex Riau
ISSN : 24434159     EISSN : 24605255     DOI : https://doi.org/10.35143/jkt
Core Subject : Science,
Computer Science & IT
Applied Computer Journal Articles from various fields in Informatics, Information Systems and Computer science. Topics included, 1. Informatics 1.1 Software Engineering 1.2 Multimedia 2. Information Systems 2.1 Soft Computing 2.2 Business Analyst 2.3 Data Engineering 3. Computer science 3.1 Operating System 3.2 Computer Network
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 229 Documents
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OPTIMASI SISTEM INFERENSI FUZZY DENGAN ALGORITMA GENETIKA UNTUK PERSONALISASI REKOMENDASI BEBAN AWAL LATIHAN DEADLIFT Naufal, Atha Redian; Hermawan, Arief
Jurnal Komputer Terapan Vol 11 No 2 (2025): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v11i2.6780

Abstract

The risk of injury in weightlifting, particularly in the deadlift movement, is often caused by subjective initial load determination and exacerbated by the egolifting phenomenon. To mitigate this risk, this research proposes an intelligent hybrid model for personalized load recommendation. This model integrates a Fuzzy Inference System (FIS) with a Genetic Algorithm (GA) to perform end-to-end parameter optimization. The fuzzy system utilizes four inputs (BMI, WHtR, RHR, Experience) to represent the user's condition. The Genetic Algorithm then automatically tunes 18 crucial system parameters, including membership functions and adjustment factors, using 20 real data points from an expert as the ground truth. The research results show that optimization using GA successfully reduced the Mean Absolute Error (MAE) significantly. The validated final model achieved an accuracy of 74.78% and an MAE of 6.37 kg, confirming that the hybrid Fuzzy-Genetic approach is a superior method for tuning quantitative recommendation systems, resulting in more precise and reliable decisions.
PENGEMBANGAN APLIKASI DESKTOP SKINCARE UNTUK REKOMENDASI PRODUK DAN PENJUALAN BERDASARKAN JENIS KULIT Lontaan, Rolly Junius; Saroinsong, Marshanda; Sumual, Monica; Masengi, Mitchelly; Mamuaja, Tiara
Jurnal Komputer Terapan Vol 11 No 2 (2025): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v11i2.6781

Abstract

Skincare products are increasingly used to address various facial skin problems, maintain skin health, and enhance self-confidence. However, the diversity of skin types and the wide range of skincare products available on the market often make it difficult for users to choose the most suitable products. To address this issue, this study developed a desktop-based skincare application equipped with a recommendation system that uses rule-based and weighted scoring methods, implemented using Python and a MySQL database. The recommendation system analyzes users’ skin types and evaluates the compatibility of product ingredients to generate more accurate recommendations. The test results show that the application can provide suitable recommendations for four skin type categories (dry, oily, sensitive, and combination) with a recommendation suitability level of 84% based on user evaluation. In addition, the application offers a product sales feature that facilitates the process from product selection to purchase. Therefore, this application serves as an effective digital solution to help users determine the right skincare products according to their specific skin needs.
ANALISIS FAKTOR DOMINAN KEBERHASILAN AKADEMIK MAHASISWA PENERIMA BEASISWA MENGGUNAKAN PRINCIPAL COMPONENT ANALYSIS Mufidah, Nur; Syarif Sihabudin Sahid, Dadang; Perdana Arifin, Satria; Ari Sandi, Wahyu
Jurnal Komputer Terapan Vol 11 No 2 (2025): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v11i2.6819

Abstract

The academic success of scholarship recipients is not only determined by their academic abilities but also by various non-academic factors that are often difficult to measure objectively. The large number of interrelated indicators presents a challenge in identifying the most dominant factors, especially when these variables exhibit high correlations and lead to data redundancy. This study aims to identify the dominant non-academic factors influencing the academic success of scholarship recipients using Principal Component Analysis (PCA). A total of 262 students completed a questionnaire consisting of 54 non-academic items that had previously undergone validity and reliability testing. PCA was employed to reduce data dimensionality and produced 38 principal components with a cumulative explained variance of 95.08%, indicating effective dimensionality reduction without significant loss of information. The loading matrix analysis revealed that psychological conditions, learning methods, major suitability, learning motivation, and financial conditions were the most dominant contributors to the principal components. These findings provide a more structured understanding of the non-academic factors that should be considered in the development and monitoring of scholarship programs.
MODEL DASHBOARD TERINTEGRASI MENGGUNAKAN SHNEIDERMAN’S INFORMATION SEEKING MANTRA UNTUK PENGOLAHAN DATA AKADEMIK, BEASISWA DAN TRACER STUDY Sandy, Kurnia; Ardiyanto, Ardiyanto; Widyasari, Yohana Dewi Lulu; Arifin, Satria Perdana
Jurnal Komputer Terapan Vol 11 No 2 (2025): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v11i2.6832

Abstract

This study aims to develop an integrated data visualization dashboard that presents academic, scholarship, and tracer study information in real time at Politeknik Caltex Riau.(PCR). Data integration is carried out through an API Gateway within a Service-Oriented Architecture (SOA), ensuring that updates from each data source can be displayed immediately on the dashboard. To support effective and intuitive data exploration, the study applies Shneiderman’s Information Seeking Mantra, which consists of three main stages: overview first, zoom and filter, and detailson-demand. The resulting dashboard provides a comprehensive overview of key institutional indicators, offers interactive filtering features, and allows users to access detailed information such as student profiles, scholarship recipients, and alumni employment data. Performance testing using Apache JMeter demonstrates that most endpoints achieve response times below 500 ms, supporting the system’s capability for real-time data presentation. The findings indicate that the dashboard improves data monitoring efficiency, facilitates cross-domain analysis, and supports institutional decision-making based on accurate and timely information. Future enhancements may include the integration of predictive analytics and a more extensive user experience evaluation.
OPTIMASI MIX PRODUKSI TOKO ROTI CHEN BAKERY DENGAN MENGGUNAKAN METODE LINEAR PROGRAMMING BERBASIS LINGO Muhammad Naufal Farras; Sisca Octarina; Fitri Maya Puspita
Jurnal Komputer Terapan Vol 12 No 1 (2026): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v12i1.6840

Abstract

Small and Medium Enterprises (SMEs), like "Chen Bakery," often struggle with figuring out the best way to plan their daily production to make the most profit. There is a gap in research because not enough use mathematical models and computer tools to help with these decisions. This study tries to help "Chen Bakery" make more money each day by finding the best amounts to produce four main products—Sweet Bread, Sugar Donut, Mini Pizza, and Chocolate Pull-Apart Bread—while taking into account how much of the main ingredients they have and how much space is available in the oven. The method used is called Linear Programming, and it was set up and solved using LINGO 20.0 software. The results show that by planning production better, the bakery can make 24.7% more profit than it does now. LINGO software works well to find the best solutions and also helps understand how changes might affect the results, making it a useful tool for small business owners.
SISTEM PREDIKSI KECOCOKAN KARIR PASCA STUDI BERBASIS RESUME SCREENING MENGGUNAKAN METODE RANDOM FOREST DAN SVM Yutika Amelia Effendi; Afnan Nadhir; Hensa Hendy Anugerah Ebenezer Situngkir; Rafka Kumara Aradea; Stephanus Chandra Setiono
Jurnal Komputer Terapan Vol 12 No 1 (2026): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v12i1.6883

Abstract

The education-job mismatch phenomenon remains a significant challenge for university graduates, where many individuals work in fields that are not aligned with their educational background and competencies. Career decision-making processes are also generally subjective and have not fully leveraged data-driven analysis. This study aims to design and implement a post-graduation career-fit prediction system based on resume screening using a machine learning approach. The proposed method employs two supervised classification algorithms, namely Random Forest and Support Vector Machine (SVM), with feature representation using TF-IDF based on n-grams on a dataset of 13,389 resumes. The results indicate that both models achieve strong performance; however, SVM outperforms Random Forest, achieving an accuracy of 84.39% and an F1-score of 83.36%, compared to 81.96% accuracy for Random Forest. Feature importance analysis reveals that technical skills, work experience, and field of study are the most influential factors in determining career fit. This study contributes a data-driven predictive approach to support more objective career decision-making for students and graduates.
Visual Analytics Pola Kerja Praktik Mahasiswa Menggunakan Tableau (Studi Kasus: Politeknik Caltex Riau) Ardiyanto Ardiyanto; Yuliska Yuliska
Jurnal Komputer Terapan Vol 12 No 1 (2026): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v12i1.6895

Abstract

The implementation of student internships is an important component of vocational education that bridges academic learning and industry needs. However, internship data are generally still presented in tabular form, making it difficult to comprehensively identify placement patterns, implementation trends, and industry characteristics. This study aims to apply a visual analytics approach to analyze trends and characteristics of student internship placements at Politeknik Caltex Riau. The data used consist of historical internship records over four academic semesters, including study programs, industry types, industry locations, internship durations, and industry partners. The research method includes data preprocessing, data transformation, and the development of an interactive visual dashboard using Tableau. The analysis results indicate fluctuating internship implementation trends across academic years, with student placements concentrated in specific industrial sectors and geographic regions. In addition, several industry partners were consistently identified as dominant internship providers. The findings demonstrate that the visual analytics approach can support more intuitive identification of internship placement patterns, improve program evaluation, and enhance data-driven decision-making in internship management.
Integrasi Explainable Artificial Intelligence (XAI) pada WebGIS untuk Audit Kepatuhan Ruang Terbuka Hijau di Provinsi Lampung Fiqih Satria; m Husaini
Jurnal Komputer Terapan Vol 12 No 1 (2026): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v12i1.6905

Abstract

The acceleration of development in Lampung Province has led to significant land-use conversion, resulting in a decline in environmental carrying capacity and non-compliance with minimum Green Open Space (GOS) requirements. This problem is exacerbated by the limitations of spatial monitoring systems, which remain descriptive in nature and are unable to provide predictive analysis or transparency in decision-making. This study aims to develop Lampung Scientific GIS, a WebGIS platform based on Explainable Artificial Intelligence (XAI) to conduct GOS compliance audits and ecological load analysis automatically and in an interpretable manner. The methods employed include data extraction from ESRI Sentinel-2 Land Cover satellite imagery using zonal statistics techniques, the development of a three-layer WebGIS architecture, and the implementation of a Rule-Based Explainable Artificial Intelligence (XAI) model to evaluate compliance with regulations and calculate the Ecological Burden Index. The research results indicate that urban areas such as Bandar Lampung City and Metro City are non-compliant with the 30% RTH mandate and face high ecological pressure. The developed system is capable of generating transparent risk assessments and data-driven policy recommendations. This research contributes to the development of an accountable spatial decision support system through the integration of WebGIS and XAI for sustainable regional planning. The novelty of this research lies in the integration of real-time, Rule-Based Explainable Artificial Intelligence (XAI) within a WebGIS environment, enabling spatial compliance audits that are not only descriptive but also interpretable, prescriptive, and transparent in supporting data-driven decision-making.
A Prototype-Driven Approach for e-Waste Redistribution Heni Rachmawati Santoso; Adriza Rafly Azlami; Yuli Fitrisia; Wenda Novayani
Jurnal Komputer Terapan Vol 12 No 1 (2026): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v12i1.6906

Abstract

The rise of electronic waste (e-Waste) poses a serious environmental challenge due to the public's lack of awareness and limited access to standardized recycling facilities. This study proposes a community-based E-Waste redistribution model integrated with Circular Economy principles, implemented through a mobile platform. To ensure responsible E-Waste management, this architecture features a dedicated redistribution flow connecting end-users directly with verified recyclers. Furthermore, Location-Based Services (LBS) are implemented not merely for mapping, but as a spatial proximity-matching algorithm using the Haversine formula to optimize logistics. The system was developed using a prototyping methodology, encompassing comprehensive user requirement identification and two iterative design cycles based on qualitative user feedback. Functional evaluation via black-box testing validated a 100% success rate across all system integration scenarios. Moreover, usability testing involving 21 demographically targeted respondents, evaluated using a custom-developed questionnaire and yielded a feasibility score of 91.5%. These findings indicate a high level of user acceptability and interface effectiveness. Ultimately, this research contributes an algorithmic and architectural framework for communitydriven e-Waste management, establishing a viable digital solution for sustainable waste redistribution.

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