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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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Sistem Pendukung Keputusan Pengadaan Alat Kesehatan Pada RS Indah Bagan Batu Dengan Menggunakan Metode Grey Absolute Decision Analysis (GADA) (Studi Kasus : RS Indah Bagan Batu) Syaputra, Panca; Juledi, Angga Putra; Munthe, Ibnu Rasyid
MEANS (Media Informasi Analisa dan Sistem) Volume 8 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54367/means.v8i1.2567

Abstract

An medical devices are instruments, apparatus,machines or implants that do not containt drugs that are used to prevent, diagnose, cure and alleviate disease, or to shape the structure and improve body functions. The problem found by researchers is how to procure medical devices that have only relied on the intuition of the management team without being based on nalysis in meeting the needs of patients and hospitals. In this researcher, the write uses the method of Gray Absolute Decision Analysis (GADA) as a decision making solution to determie procurement of health equipment. This method is considered as the procurement of medical device because the dual method performs the drying process based on different attributes and weights and prioritizes and weights and prioritizes alternative data so that the results are more optimal and accurate in the procurement of medical devices.
Implementasi Data Mining Algoritma Apriori untuk Meningkatkan Penjualan Harist N, Abdul; Munthe, Ibnu Rasyid; Juledi, Angga Putra
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 6 No. 1 : Tahun 2021
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1265.802 KB) | DOI: 10.54367/jtiust.v6i1.1276

Abstract

Setiap perusahaan atau organisasi yang ingin bertahan perlu menentukan strategi bisnis yang tepat. Data penjualan produk yang dilakukan oleh Lakoe Dessert Pondok Kacang pada akhirnya akan menghasilkan data yang menumpuk, sehingga sangat disayangkan jika tidak dianalisis kembali. Produk yang ditawarkan bervariasi dengan variasi produk sebanyak 45 produk, untuk mengetahui produk yang paling banyak penjualannya dan keterkaitan antara produk yang satu dengan produk yang lain diperlukan salah satu algoritma dalam algoritma data mining yaitu apriori algoritma untuk mengetahuinya, dan dengan bantuan aplikasi Rapidminer 5, dengan nilai dukungan 2,4% dan nilai kepercayaan 50%, produk yang sering dibeli atau diminati pelanggan dapat ditemukan. Penelitian ini menggunakan data penjualan bulan Maret 2020 yang berjumlah 209 data transaksi. Dari penelitian tersebut, ditemukan item dengan nama Pudding Strawberry dan Pudding Vanilla merupakan produk yang paling banyak dibeli oleh konsumen. Dengan mengetahui produk yang paling banyak terjual dan pola pembelian barang yang dilakukan oleh konsumen, Lakoe Dessert Pondok Kacang dapat mengembangkan strategi pemasaran untuk memasarkan produk lain dengan menganalisis keuntungan dari penjualan produk yang paling banyak terjual dan mengantisipasi kehabisan atau kosongnya stok atau bahan pada suatu saat. tanggal kemudian
Sosialisasi Pencegahan Kekerasan Seksual dan Perlindungan HAM melalui Pendekatan Participatory Action Research di SDN 12 Sidorukun Adam, Dini Hariyati; Anjar, Agus; Hasibuan, Elysa Rohayani; Rohani, Rohani; Juledi, Angga Putra; Nazliah, Rahmi; Masrizal, Masrizal
Jurnal Pengabdian Masyarakat: Pemberdayaan, Inovasi dan Perubahan Vol 5, No 4 (2025): JPM: Pemberdayaan, Inovasi dan Perubahan
Publisher : Penerbit Widina, Widina Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59818/jpm.v5i4.1762

Abstract

Sexual violence is an act of intentionally insulting, degrading, harassing, or physically assaulting someone, which can harm someone. This outreach program aims to increase students' knowledge of sex education and how adolescents can prevent sexual harassment. This community service program uses the Participatory Action Research (PAR) method, a method that involves engaging with students. This activity is carried out directly, starting with observation, coordination, and obtaining permission from the local school principal regarding the activity plan. This includes arrangements regarding the availability of space, time, and participants. The material presented relates to insights into sexual violence, types of sexual violence, the impacts of sexual violence, and laws that regulate and protect victims of sexual violence. The role of schools in preventing sexual violence and protecting students' human rights has not been optimally implemented due to the lack of specific regulations and the formation of an anti-violence task force. Furthermore, school efforts also lack a structured handling process. Therefore, it is necessary to provide education and knowledge regarding the laws that regulate and protect victims of sexual violence. The results of this activity show an increase in knowledge, affective skills, and understanding regarding cases of sexual violence because preventing sexual violence is a shared responsibility that requires the active involvement of all elements of society.ABSTRAKKekerasan seksual merupakan salah satu tindakan yang secara sengaja menghina, merendahkan, melecehkan, dan menyerang fisik, secara sengaja yang dapat merugikan seseorang. Sosialisasi ini bertujuan untuk menambah tingkat pengetahuan siswa terkait edukasi seks, serta bagaimana sikap remaja mencegah terjadinya pelecehan seksual. Pengabdian ini menggunakan metode PAR (Participatory Action Research) yakni sebuah metode dengan cara pendekatan terhadap siswa-siswi. Kegiatan ini dilaksanakan secara langsung dimulai dengan melakukan observasi, koordinasi, dan perizinan dengan kepala sekolah setempat terkait rencana kegiatan. Hal ini mencakup pengaturan mengenai ketersediaan tempat, waktu, dan peserta. Materi yang dipaparkan terkait wawasan mengenai kekerasan seksual, jenis-jenis kekerasan seksual, dampak kekerasan seksual serta hukum yang mengatur dan melindungi korban kekerasan seksual. Peranan sekolah dalam upaya pencegahan kekerasan seksual dan perlindungan HAM pada siswa belum dapat dilaksanakan maksimal, disebabkan belum tersedianya peraturan khusus dan satgas anti kekerasan yang dibentuk. Selain itu, upaya sekolah juga belum memiliki alur penanganan yang terstruktur. Sehingga perlu diberikan edukasi dan pengetahuan terkait hukum yang mengatur dan melindungi korban kekerasan seksual. Hasil kegiatan ini memperlihatkan adanya peningkatan pengetahuan, afektif serta pemahaman terkait kasus kekerasan seksual karena pencegahan kekerasan seksual merupakan tanggung jawab bersama yang memerlukan keterlibatan aktif dari seluruh elemen masyarakat.
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.
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 Harahap, Tongku Hamonangan 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 Naibaho, Restu Fauzy 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 Tomi Hidayat, Tomi Yudi Triyanto Yuhandri Yuhandri, Yuhandri Yunus, Yuhandri Zebua, Yuniman ZURAIDAH ZURAIDAH