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All Journal JIPN (Journal of Informatics Pelita Nusantara) Jurnal Nasional Komputasi dan Teknologi Informasi TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Journal of Intelligent Decision Support System (IDSS) INFOKUM Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Pengabdian kepada Masyarakat Nusantara KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Info Sains : Informatika dan Sains BIOS: Jurnal Informatika dan Sains Jurnal Pengabdian Kepada Masyarakat Teknologi Informasi dan Komunikasi
Hasugian, Penda Sudarto
STMIK Pelita Nusantara, Medan
Aerospace Engineering Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Neuroscience Social Sciences Transportation

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The Application of C4.5 Algorithm to Prediction Sales at PT. Sumber Sayur Segar Fadhila Fadhila; Penda Sudarto Hasugian
Journal of Intelligent Decision Support System (IDSS) Vol 5 No 1 (2022): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v5i1.45

Abstract

Fresh vegetables, fruits and fresh meat are one of the basic needs for human life. The need for fresh vegetables, fruits and meat is one of the most important factors for buyers before making a purchase transaction. Likewise with the needs of fresh vegetables, fruit and meat needed by restaurants, cafes, hospitals, hotels and so on. With the increasing number of requests from customers for the needs of fresh vegetables, fruit and meat, companies engaged in the supply and sale of these necessities need to record sales transactions so that there are no stock vacancies and excess stock of goods. Therefore, companies must be more careful in providing fresh vegetables, fruits and meat which are in great demand, so it needs a data processing in the form of data mining using the C4.5 algorithm. In this study, the predicted sales transactions are the last three months of January, February and March 2021. Then for the sales prediction criteria used are in the form of price, type of goods, type of unit and month of sale so that from these criteria can be obtained sales transactions that are selling or not selling. Data mining is a process of mining important information from a very large data. While the C4.5 algorithm is a data classification that has numeric and categorical attributes, where the results of the classification process in the form of rules can be used to predict the value of discrete type attributes from new records. The system was built using the PHP programming language and MySQL as the database. This study obtained predictive results which were implemented in the form of a decision tree, namely the category of types of vegetables belonging to the best-selling sales transactions.
Expert System to Diagnose Eye Disease Due to Frequently Using Computer with Bayes Theorem Method Taozara Laia; Penda Sudarto Hasugian
Journal of Intelligent Decision Support System (IDSS) Vol 5 No 1 (2022): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v5i1.46

Abstract

Eye disease due to frequent use of computers is one of the dangerous diseases in health because if not treated quickly it will result in blindness. These eye diseases can be diagnosed through the symptoms that arise in humans or through their clinical picture, through these symptoms an expert system can be made to make a diagnosis. An expert system is a system that seeks to adopt human knowledge to a computer that is built to solve problems like an expert. The expert system made in carrying out the diagnosis uses the Bayes theorem method to calculate the probability of an event occurring based on the influence obtained from the results of observations and experts. The system was built using PHP and MySQL programming as a database. The method used for tracing is Bayes' theorem. While the results of the diagnosis will inform about the results of the diagnosis containing a list of symptoms entered, information on the results of the rules regarding the eye disease suffered and information about possible treatments that can be carried out as well as treatment solutions.
WEBSITE DESIGN TO SUPPORT LEARNING SYSTEM IN PRIVATE SD PUTRI DELI NAMORAMBE Penda Sudarto Hasugian; Jijon Raphita Sagala
INFOKUM Vol. 10 No. 03 (2022): August, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (422.867 KB)

Abstract

Education is a process of communication and information from educators to students which contains information about education, which has elements of educators as sources of information, media as a means of presenting ideas, ideas and educational materials as well as students themselves, some elements of This approach gets a touch of information technology media, thus sparking the birth of the idea of ​​e-learning. Deli Namorambe Putri Private Elementary School is still carrying out the direct learning process, where the entire teaching and learning process between teachers and students can only be done with class meetings. If there is no meeting between students and teachers in the classroom, the learning process will not occur. This causes the delivery of material and assignments to be disrupted so that it can result in a lack of student understanding of a learning material. The development of this system uses the PHP programming language, xampp as a local web server on the computer. With this system, it can help teachers and students in the teaching and learning process so that it can be used to fill out attendance lists, upload teaching materials, download teaching materials, take quizzes, send assignments and exams.
Penerapan Data Mining Untuk Pengelompokan Siswa Berdasarkan Nilai Akademik dengan Algoritma K-Means Penda Sudarto Hasugian; Jijon Raphita Sagala
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 3 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i3.627

Abstract

The data mining process by applying the K-Means algorithm is carried out to group data into one or more groups, where data that has representative similarities is grouped into one group and data that has differences is included in another group. Grouping student data is done to facilitate schools in facilitating students based on differences in their ability to learn and participate in learning which consists of groups or classes of superior students, medium and low groups. The data application used for the calculation process is student data based on a centralized assessment in presenting reports on student learning outcomes using the results of report cards, namely the rapid miner. This assessment forms the basis of the attributes used in the calculation process to determine superior, medium and low class students. The purpose of this study is to manage centralized assessment data in presenting reports on student learning outcomes and grouping students in superior classes by implementing the K-means algorithm and conducting tests using the rapidminer application. So that student data can be managed and grouped effectively and efficiently
Alumni Data Grouping Using the K-Means Clustering Method for Study Program Curriculum Development Penda Sudarto Hasugian; Jijon Raphita Sagala; Lela Dwi Ani
Jurnal Info Sains : Informatika dan Sains Vol. 13 No. 02 (2023): Jurnal Info Sains : Informatika dan Sains , Edition September  2023
Publisher : SEAN Institute

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Abstract

Application of Datamining by applying the k-means clustering method to classify STMIK Pelita Nusantara alumni data as a basis for developing study program curricula that are more relevant to the needs of the world of work or industrial needs. Where the K-Means Clustering Method is used to group alumni based on similar characteristics they have, such as personal data, academic achievement, areas of expertise, and job information after graduating from college. The research data source used is graduate data for the 2021/2022 academic year. The data collection method was carried out by distributing questionnaires directly to alumni. The application of the k-means method is carried out by forming 2 groups (clusters), namely C1 = Liner and C2 = Not Linear. Data testing is also carried out using the rapid miner application. So that by grouping alumni data, it is hoped that tertiary institutions can identify the needs and preferences of alumni for the study programs followed so that they can develop study program curricula that are more targeted and in accordance with the needs of the job market.Application of Datamining by applying the k-means clustering method to classify STMIK Pelita Nusantara alumni data as a basis for developing study program curricula that are more relevant to the needs of the world of work or industrial needs. Where the K-Means Clustering Method is used to group alumni based on similar characteristics they have, such as personal data, academic achievement, areas of expertise, and job information after graduating from college. The research data source used is graduate data for the 2021/2022 academic year. The data collection method was carried out by distributing questionnaires directly to alumni. The application of the k-means method is carried out by forming 2 groups (clusters), namely C1 = Liner and C2 = Not Linear. Data testing is also carried out using the rapid miner application. So that by grouping alumni data, it is hoped that tertiary institutions can identify the needs and preferences of alumni for the study programs followed so that they can develop study program curricula that are more targeted and in accordance with the needs of the job market.
Sistem Pendukung Keputusan Penentuan Penerima Bantuan Bedah Rumah Menggunakan Metode Weighted Product Pada Kecamatan Borbor Wasindo Hutahaean; Penda Sudarto Hasugian
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 4, No 1 (2021): Februari 2021
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v4i1.2751

Abstract

In rural areas there are still many residents 'houses that are unfit for habitation and this is due to the factor of the standard of living of people who are still living simply and have below average income, these conditions make many residents' houses unfit for habitation that need to be repaired through assistance Home renovation. The Weighted Product method can help in making decisions, but the calculation using the Weighted Product method only produces the largest value which will be selected as the best alternative. The design of decision-making for housing renovation assistance recipients at the Housing and Settlement Areas is based on criteria, namely Indonesian citizens, below average income, belonging to a family, owning or controlling land, not having a house or owning and living in an uninhabitable house using the Weighted Product method. Application design using the programming language PHP and MySQL
THE DATA MINING OF CELL PHONE MOST INTERESTED USING APRIORIAL ALGORITHM Penda Sudarto Hasugian; Suprianto Panjaitan
BIOS: Jurnal Informatika dan Sains Vol. 1 No. 01 (2023): BIOS: Jurnal Informatika dan Sains, April 2023
Publisher : Sean Institute

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Abstract

Data mining is a term used to describe knowledge discovery in a database or often called Knowledge Discovery in Database (KDD). With the development of information at this time, the need for accurate information is needed in daily life, so that information will become an important element in the development of society today and the future. However, high information needs are sometimes not balanced by the presentation of adequate information, often the information must still be extracted from very large amounts of data. The ability of information technology to collect and store various types of data far leaves the ability to analyze, summarize and extract knowledge from data. Decision-makers try to utilize data warehouse that has been owned to dig up information that is useful to help make decisions, this encourages the emergence of new branches of science to overcome the problem of extracting information or patterns that are important or interesting from large amounts of data, which is called data mining. The use of data mining techniques is expected to provide knowledge previously hidden in the data warehouse so that it becomes valuable and useful information.
Pengembangan Perangkat Lunak Sistem Deteksi Dini Kesulitan Belajar Siswa Berbasis Artificial Intelligence Computing Platform Jijon Raphita Sagala; Erwin Panggabean; Penda Sudarto Hasugian; Prasanth Kumar; Aispriyani
Jurnal Pengabdian Kepada Masyarakat Teknologi Informasi dan Komunikasi Vol 2 No 1 (2025): Januari
Publisher : CV. ADMITECH SOLUTIONS

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Abstract

Pengabdian ini bertujuan untuk mengembangkan perangkat lunak sistem deteksi dini kesulitan belajar siswa di sekolah dasar berbasis Artificial Intelligence Computing Platform. Sistem ini dirancang untuk membantu pendidik dan orang tua dalam mengidentifikasi siswa yang mengalami kesulitan belajar secara cepat dan akurat. Dengan memanfaatkan teknologi kecerdasan buatan, perangkat lunak ini mampu menganalisis data akademik, perilaku, serta faktor psikologis siswa untuk mendeteksi potensi permasalahan belajar yang mungkin muncul. Dalam penerapannya, sistem ini akan mengumpulkan data dari siswa secara real-time melalui platform AI, mengolah informasi tersebut dengan algoritma pembelajaran mesin (machine learning), dan menyajikan laporan hasil deteksi dini dalam bentuk yang mudah dipahami oleh guru dan orang tua. Pendekatan ini diharapkan dapat memberikan dukungan intervensi yang tepat waktu sehingga siswa yang mengalami kesulitan dapat memperoleh bantuan lebih cepat. Program pengabdian ini diimplementasikan di lingkungan sekolah dasar sebagai bagian dari upaya peningkatan kualitas pendidikan dan dukungan pembelajaran yang inklusif. Evaluasi awal menunjukkan bahwa perangkat lunak ini dapat memberikan hasil analisis yang akurat, mempercepat identifikasi siswa dengan kesulitan belajar, serta mempermudah tenaga pendidik dalam merancang strategi pembelajaran yang lebih efektif.
Pengembangan Perangkat Lunak Identifikasi Area Kesulitan Belajar Siswa Sekolah Dasar SD Negeri 064022 Medan Menggunakan Clustering dan Association Rule Mining Erwin Panggabean; Jijon Raphita Sagala; R. Mahdalena Simanjorang; Yuda Perwira; Penda Sudarto Hasugian; Pria Dimas; Hartati Palentina Sipahutar
Jurnal Pengabdian Kepada Masyarakat Teknologi Informasi dan Komunikasi Vol 2 No 3 (2025): Juli
Publisher : CV. ADMITECH SOLUTIONS

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Abstract

Kesulitan belajar yang dialami oleh siswa sekolah dasar sering kali tidak terdeteksi sejak awal, yang berdampak pada rendahnya prestasi akademik serta keterlambatan dalam pemberian intervensi yang sesuai. Penelitian ini bertujuan merancang dan mengembangkan sebuah perangkat lunak untuk mengidentifikasi area kesulitan belajar siswa di SD Negeri 064022 Medan, dengan menerapkan metode Clustering dan Association Rule Mining. Algoritma K-Means dimanfaatkan dalam proses Clustering guna mengelompokkan siswa berdasarkan hasil belajar dan karakteristik individual. Di sisi lain, teknik Association Rule Mining digunakan untuk menemukan pola keterkaitan antara faktor-faktor yang berpengaruh terhadap kesulitan belajar, seperti kehadiran, minat terhadap pelajaran, serta pencapaian nilai akademik. Evaluasi yang dilakukan menunjukkan bahwa sistem mampu melakukan klasifikasi siswa berdasarkan tingkat potensi kesulitan belajar dengan tingkat akurasi yang memadai. Selain itu, sistem juga menghasilkan sejumlah aturan asosiatif yang dapat dimanfaatkan oleh guru sebagai acuan dalam proses pengambilan keputusan. Dengan demikian, aplikasi ini berpotensi menjadi sarana pendukung yang efisien dalam proses pendeteksian dan penanganan kesulitan belajar secara terstruktur serta berbasis data yang terukur.  
Co-Authors Agustina Simangunsong Aispriyani Amran Sitohang Anzelia Anzelia Arjon Samuel Sitio Dhini Andika Diana Sari Muliani Saragih Erwin Panggabean Fadhila Fadhila Feryanto Ginting Fristi Riandari Hartati Palentina Sipahutar Hasugian , Paska Marto IRMA YOLANDA SITORUS Jijon Raphita Sagala Jijon Raphita Sagala John Foster Marpaung Kristiani Romili Sitanggang Lela Dwi Ani Lincah Situmorang Prasanth Kumar Pria Dimas R. Mahdalena Simanjorang R. Mahdalena Simanjorang Simangunsong, Agustina Sulindawaty, Sulindawaty Suprianto Panjaitan Taozara Laia Wasindo Hutahaean Yuda Perwira