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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Informatika UNIKA Santo Thomas Building of Informatics, Technology and Science Informatika Agro Bali: Agricultural Journal Jurnal Sistim Informasi dan Teknologi Jurnal Tekinkom (Teknik Informasi dan Komputer) MEANS (Media Informasi Analisa dan Sistem) Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan (JUSTIKPEN) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Journal of Computer Science and Information Systems (JCoInS) IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT Jurnal Pengabdian Masyarakat Bhinneka Internet of Things and Artificial Intelligence Journal
Juledi, Angga Putra
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Implementasi K-Means Dalam Menentukan Tingkat Kepuasan Pelanggan Pada Bengkel Rizal Rantauprapat Rambey, Khiarul Akhyar; Suryadi, Sudi; Harahap, Syaiful Zuhri; Juledi, Angga Putra
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7937

Abstract

The growing automotive industry demands workshops to improve the quality of service for customer satisfaction. However, manual measurement of satisfaction is often inefficient and subjective. This study proposes the application of machine learning algorithms K-Means Clustering to analyze customer satisfaction data in Rizal workshop. This method is used to Group customers into several clusters based on similar satisfaction characteristics. The results of this grouping are expected to provide more objective and in-depth insights to identify patterns of satisfaction, thus enabling the workshop to formulate a more effective and targeted service quality improvement strategy.
Penerapan Data mining Klasifikasi Tingkat Kepuasan Mahasiswa Terhadap Pelayanan Akademik Menggunakan Metode Naïve Bayes Dan Support Vector Machine (Studi Kasus Program Studi Sistem Informasi Universitas Labuhanbatu) Antika, Dewi; Harahap, Syaiful Zuhri; Ah, Rahma Muti; Juledi, Angga Putra
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7917

Abstract

This study was conducted to classify public satisfaction levels using the Support Vector Machine (SVM) algorithm as the primary data analysis method. The objective of this study was to obtain an accurate and reliable prediction model for determining the Satisfaction and Dissatisfaction categories based on the available data. The theoretical basis used refers to the concept of machine learning, specifically SVM, which works by forming an optimal hyperplane to separate data classes. In addition, model evaluation theories such as the Confusion Matrix were used to objectively measure prediction performance. The research methodology included data collection, pre-processing, dividing the dataset into training and test data, and training the SVM model. Evaluation was conducted using accuracy, sensitivity, and specificity metrics to assess the model's ability to predict data accurately. The results and discussion indicate that the SVM successfully classified the majority of data correctly, with the Satisfaction class having a perfect prediction rate while the Dissatisfaction class still had a small error. Further analysis indicated the need for SVM parameter optimization to improve accuracy in the minority class. The conclusion of this study states that the SVM has good performance in classifying public satisfaction data, although it still requires refinement in recognizing certain class patterns. This finding opens up opportunities for developing more adaptive methods to improve predictive performance.
Penerapan Algoritma Random Forest untuk Klasifikasi Tingkat Keparahan Penyakit pada Data Rekam Medis Nasution, Fitri Aini; Juledi, Angga Putra
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7993

Abstract

Accurate determination of disease severity is an important step in supporting medical decision-making. This study aims to classify the severity of patients’ diseases into three categories—Mild, Moderate, and Severe—using the Random Forest algorithm. The data used were obtained from patients’ medical records containing structured clinical parameters and have undergone a preprocessing stage, including data cleaning, variable transformation, and splitting into training data (80%) and testing data (20%). The test results show that the Random Forest model achieved an accuracy of 74.77%. The best performance was obtained in the Mild class with a recall value of 0.95 and an f1-score of 0.84. The Moderate class achieved a recall of 0.71 and an f1-score of 0.73, while the Severe class showed perfect precision (1.00) but a low recall (0.12), indicating the model’s limited ability to detect cases in this class. The macro average values for precision, recall, and f1-score were 0.83, 0.60, and 0.59 respectively, while the weighted average values were 0.78, 0.75, and 0.71 respectively. These findings indicate that Random Forest can be used to classify disease severity based on medical records with relatively good performance for the majority class, but further optimization—such as data balancing or parameter adjustment—is needed to improve sensitivity toward classes with fewer samples.
Optimalisasi Kinerja Tenaga Kependidikan di MTSN 1 Labuhanbatu Selatan Studi Kasus Penggunaan Algoritma Naïve Bayes Rambe, Aida Zahrah Hasanati Br; Juledi, Angga Putra; Irmayani, Deci; Harahap, Syaiful Zuhri
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.8034

Abstract

This study aims to optimize the performance of Education personnel in MTsN 1 Labuhanbatu Selatan through the application of Naive Bayes algorithm for performance classification. The performance of Education personnel, including administrative, administrative, and service staff for one school year was analyzed using data involving attributes such as attendance, punctuality, productivity, and work attitude. Naive Bayes algorithm was chosen because of its ability to classify data accurately and efficiently despite the large amount of data. The results showed that the use of this algorithm can produce a more objective, accurate, and data-based evaluation system, as well as provide clearer insights in improving work efficiency and service to teachers and students. The evaluation of the model was conducted using accuracy, precision, recall, and F1-score metrics to ensure that the classification of educational staff performance can be done appropriately. The study also provides recommendations to improve data quality and the use of additional attributes to improve model performance.
Optimisasi Manajemen Sumber Daya pada Sistem Operasi C untuk Lingkungan Cloud Computing Huda, Nurul; Rasyid Munthe, Ibnu; Juledi, Angga Putra
Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Vol. 7 No. 1 (2024): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jikomsi.v7i1.2721

Abstract

Dengan perkembangan pesat teknologi cloud computing, manajemen sumber daya menjadi kritis untuk memastikan kinerja optimal sistem operasi. Sistem Operasi C memiliki peran penting dalam lingkungan cloud computing untuk mendukung aplikasi yang berjalan di atasnya. Penelitian ini bertujuan untuk mengoptimalkan manajemen sumber daya pada Sistem Operasi C agar dapat memenuhi tuntutan lingkungan cloud computing yang dinamis. Penelitian ini fokus pada pengembangan teknik dan strategi untuk meningkatkan alokasi, pemantauan, dan penggunaan sumber daya secara efisien. Kami memanfaatkan algoritma manajemen sumber daya yang adaptif dan dinamis untuk menyesuaikan alokasi sumber daya berdasarkan beban kerja dan kebutuhan aplikasi. Selain itu, kami mengimplementasikan mekanisme pemantauan yang canggih untuk mendeteksi anomali dan memprediksi kebutuhan sumber daya di masa depan. Metode eksperimen dilakukan menggunakan lingkungan simulasi yang mencerminkan kondisi nyata lingkungan cloud computing. Hasil eksperimen menunjukkan peningkatan signifikan dalam kinerja sistem operasi, dengan pengurangan waktu respons dan peningkatan efisiensi penggunaan sumber daya. Penelitian ini memberikan kontribusi pada pengembangan sistem operasi yang dapat mengoptimalkan manajemen sumber daya dalam konteks lingkungan cloud computing. Dengan demikian, hasil penelitian ini dapat menjadi landasan bagi pengembangan lebih lanjut dalam meningkatkan efisiensi dan kinerja sistem operasi C dalam mendukung aplikasi cloud.
Memanfaatkan Algoritma Apriori: Aplikasi Berbasis Web untuk Penambangan Aturan Asosiasi Siddik, Rasid; Juledi, Angga Putra; Sihombing, Volvo
Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Vol. 7 No. 1 (2024): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penambangan pola asosiasi telah menjadi topik yang menarik dalam penelitian data mining karena kemampuannya untuk mengidentifikasi hubungan yang tersembunyi dalam data transaksional. Dalam konteks ini, pengembangan aplikasi berbasis web untuk penambangan pola asosiasi menjadi relevan untuk memfasilitasi analisis data yang lebih mudah dan lebih cepat. Metode Apriori, yang telah terbukti efektif dalam menemukan pola asosiasi, diimplementasikan dalam aplikasi ini. Aplikasi web ini dirancang untuk memberikan antarmuka pengguna yang intuitif dan fitur yang memungkinkan pengguna untuk melakukan penambangan pola asosiasi dengan mudah. Penelitian ini mendokumentasikan proses pengembangan aplikasi, termasuk perancangan antarmuka, implementasi metode Apriori, dan pengujian fungsional. Hasilnya menunjukkan bahwa aplikasi web yang dikembangkan mampu melakukan penambangan pola asosiasi dengan efisien dan efektif, dengan memberikan hasil yang relevan dan berguna bagi pengguna dalam pemahaman lebih lanjut terhadap data transaksional yang mereka miliki
Implementasi Data Mining dengan Menggunakan Algoritma Apriori untuk Mengoptimalkan Pola Penjualan Produk Elektronik Dewi, Rahayu Kusnita; Juledi, Angga Putra; Irmayani, Deci
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7515

Abstract

This study discusses the application of the Apriori algorithm in analyzing electronic product sales data. The results show that the Apriori algorithm is effective in finding consumer purchasing patterns through association analysis, which allows the identification of product combinations that are often purchased together. Combinations of products with strong purchasing relationships, such as AAA Batteries (4-pack) and USB-C Charging Cable (confidence 0.9), and Wired Headphones and USB-C Charging Cable (confidence 0.7), can be utilized for bundling strategies and increasing sales. Of the 18 types of electronic products analyzed, seven products met the minimum support requirements, indicating high potential for further analysis. The Apriori algorithm also proved suitable for medium-scale datasets due to its simplicity, although it is less efficient than FP-Growth on big data. This study concludes that the application of the Apriori algorithm supports data-based business decision making, especially in understanding consumer behavior, stock management efficiency, and marketing strategy development.
Sistem Pendukung Keputusan Pemilihan Calon Ketua Komite Sekolah Menggunakan Metode CoCoSo Bilatasya, Yolanda; Juledi, Angga Putra; Irmayani, Deci; Harahap, Syaiful Zuhri
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7530

Abstract

The election of the school committee chairperson is one of the important decision-making processes that directly impacts the sustainability and effectiveness of collaboration between the school and parents/guardians. At SMP Negeri 1 Rantau Selatan, the selection process for the committee chairperson has been conventional, subjective, and lacks a structured and standardised assessment system. This results in the selection process being based on popularity and personal connections rather than objective competence and qualifications. To address this issue, this study aims to design and implement a Decision Support System (DSS) based on the Combined Compromise Solution (CoCoSo) method, which can accommodate various quantitative evaluation criteria and generate more objective, transparent, and accountable decision recommendations. The CoCoSo method was chosen for its ability to integrate a compromise approach to conflicting criteria and produce consistent alternative rankings through three aggregation techniques: arithmetic mean, relative sum, and compromise programming. This study uses five main criteria to assess the suitability of committee chair candidates: experience in the field of education, communication skills, leadership, understanding of education policy, and integrity. Data was obtained from 15 committee chair candidates based on observation and questionnaire results, which were then processed through the CoCoSo method stages, including decision matrix formation, value normalisation, positive and negative ideal solution calculations, and final score aggregation. The data processing results show that the candidate named Eko Prasetyo obtained the highest compromise value in all CoCoSo calculation approaches with a final Ki value of 2.505, consistently placing him as the top-ranked candidate in the system's recommendations. This demonstrates that the CoCoSo method is effective in evaluating and determining the best candidate based on a data-driven and scientifically rational approach. Additionally, the system built can also serve as a strategic tool to enhance the quality of participatory educational governance at the school unit level.
Pelatihan Digital Marketing Pada Usaha Mikro Kecil Menengah (UMKM) diKelurahan Negri Lama Masrizal; Zebua, Yuniman; Juledi, Angga Putra; Fitri, Juli Hati; Rambe, Inny Rahayu
Jurnal Pengabdian Masyarakat Bhinneka Vol. 1 No. 2 (2022): Bulan November
Publisher : Bhinneka Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58266/jpmb.v1i2.17

Abstract

Salah satu bidang usaha yang tetap konsisten dan berkembang dalam perekonomian nasional salah satunya adalah Usaha Mikro, Kecil, dan Menengah (UMKM). Dengan maraknya pertumbuhan usaha saat ini persaingan pasarpun semakin ketat.Peralihan tren pemasaran dari konvensional (offline) ke media online menyebabkan para pelaku UMKM yang tidak memanfaatkan potensi digital marketing kehilangan kesempatan untuk lebih mengembangkan bisnisnya. Sosialisasi ini diikuti oleh 21 (Duapuluh Satu) orang para pelaku UMKM yang berada di Kelurahan negri lama kabupaten labuhanbatu. Para pelaku UMKM di sini masih kurang inovatif dalam mengembangkan usahanya. Umumnya, mereka belum menggunakan daya pikir yang kreatif untuk menggunakan digital marketing dengan memanfatkan jejaring sosial sebagai media pemasaran.Sosialisasi ini dilakukan dengan dengan tujuan untuk membentuk usaha kecil yang produktif dan mengikuti perkembangan zaman. Para peserta sosialisasi sangat antusias dengan materi dan pemaparan yang disampaikan.Kondisi ini diharapkan dapat memotivasi para pelaku UMKM mempraktekkan ilmu yang didapat, sehingga usahanya semakin berkembang dan dapat bersaing pada pasar yang lebih luas.
Discriminating Estate and Smallholder Agricultural Systems Using a Multi-Sensor Agriculture-Weighted Object-Based Classification Framework in Tropical Landscapes Triyanto, Yudi; Siregar, Jerry Maulana; Sari, Risna Maya; Juledi, Angga Putra
Agro Bali : Agricultural Journal Vol 8, No 3 (2025)
Publisher : Universitas Panji Sakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37637/ab.v8i3.2530

Abstract

The transformation of tropical landscapes due to agricultural expansion constitutes a significant global environmental challenge. Current land-cover classification methods, however, provide limited differentiation among agricultural management systems. This study develops an agriculture-focused land-cover classification workflow that fuses Landsat 9 optical imagery and PALSAR-2 L-band SAR across a ≈2,500 km² study area in Jambi Province, Sumatra, Indonesia, to enhance discrimination of crop systems and improve spatial coherence via object-based enhancement. A 22-class land-cover taxonomy was supported by 14,029 strategically collected training points. Feature engineering produced 29 predictor variables, including conventional vegetation indices, agricultural-specific metrics, water indicators, and SAR-derived structural features. Models were evaluated on an independent test dataset comprising 4,209 samples. An agriculture-weighted Random Forest classifier with strategic class weighting was implemented and followed by Simple Linear Iterative Clustering (SLIC) object-based enhancement to suppress speckle and enforce spatial contiguity. The classification achieved an overall accuracy of 53.7%, with exceptional performance for estate crop systems (F1 = 94%) and reliable forest discrimination. SLIC reduced salt-and-pepper noise by 99.5% and substantially improved spatial coherence metrics, transforming fragmented pixel-based outputs into operationally viable products. Despite these gains, discriminating smallholder mosaics remains challenging and likely requires additional temporal or higher-resolution inputs.
Co-Authors Ah, Rahma Muti Alam, Dewi Pathimah Andriyani, Wahyu Fitri Angreani Hulu, Linca Elma Popi Anjar, Agus ANTIKA, DEWI Aris Munandar Aurianda, Rieke Bilatasya, Yolanda Dewi, Rahayu Kusnita Dini Hariyati Adam Fitri, Juli Hati Harefa, Sinema Harist N, Abdul Hasibuan, Elysa Rohayani Hia, Faomaha Irmayani, Deci Lubis, Rapida Hanim Manullang, Dosma Parningotan Masrizal Masrizal Munthe , Ibnu Rasyid Munthe, Ibnu Rasyid Muti'ah, Rahma Mutia, Rahma Muti’ah, Rahma Nasution, Fitri Aini Nasution, Intan Baiduri Nasution, Marnis Nazliah, Rahmi Nurul Huda Patriya, Angga Prayoga Pransiska, Apprillia Yudha PRATAMA, ZAKARIA Putri, Kirana Dwi Rambe, Aida Zahrah Hasanati Br Rambe, Inny Rahayu Rambey, Khiarul Akhyar Rasyid Munthe, Ibnu Rezeki, Imam Dwi Ritonga, Muhammad Zakuan Rohani Rohani Sari, Risna Maya Sarjon Defit Siddik, Rasid Sihombing, Volvo Sipahutar, Alwidahyani Sipahutar, Rizka Nurfatni Siregar, Jerry Maulana Subianto, Roby Suryadi, Sudi Syahfitri, Nur’aini Indah Syaiful Zuhri Harahap Syaputra, Panca Yudi Triyanto Yuhandri Yuhandri, Yuhandri Zebua, Yuniman ZURAIDAH ZURAIDAH