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All Journal Jurnal Mantik Penusa JIPN (Journal of Informatics Pelita Nusantara) Journal of Intelligent Decision Support System (IDSS) Jurnal Ilmiah Multidisiplin Indonesia
Penda Sudarto Hasugian
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Engineering Physics

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DATA MINING TINGKAT PESANAN INVENTARIS KANTOR MENGGUNAKAN ALGORITMA APRIORI PADA KEPOLISIAN DAERAH SUMATERA UTARA Efrika Manurung; Penda Sudarto Hasugian
Journal Of Informatic Pelita Nusantara Vol 4 No 2 (2019): Computer Science
Publisher : STMIK Pelita NUsantara

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Abstract

Dalam pengadaan barang/inventaris harus selalu teliti dalam proses penyimpanan untuk digunakan selanjutnya untuk diproses data sebagai laporan atau keingin tahuan tingkat pesanan pada inventaris dengan menggunakan proses data mining. Data mining merupakan penggalian data untuk mendapatkan output/hasil yang diolah dari penyimpanan yang lama. Sama halnya dengan Kepolisian Daerah Sumatra Utara atau Polda Sumatra Utara (dulu bernama Komando Daerah Kepolisian II/Sumatra Utara), biasa disebut pula Poldasu, merupakan pelaksana tugas Polri di wilayah Provinsi Sumatra Utara yang ingin mengetahui tingkat pesanan inventaris/barang harus menggunakan proses data mining dengan bantuan algoritma apriori. Dari penjelasan inilah penelis melakukan penelitian dengan judul data mining tingkat pesanan inventaris kantor menggunakan algoritma apriori pada kepolisian daerah sumatera utara. Adapun hasil dari penelitian ini adalah aplikasi yang dibangun dapat dijadikan acuan bagi pihak instansi dalam pengambilan keputusan di masa yang akan datang.Kata Kunci : Data Mining, Algoritma Apriori, Inventaris
RANCANG BANGUN PERANGKAT CERDAS UNTUK DETEKSI KANDUNGAN UNSUR TANAH DALAM MENENTUKAN JENIS TANAMAN UNTUK MENDUKUNG KETAHANAN PANGAN Penda Sudarto Hasugian; Relima Mahdalena Simanjorang
Jurnal Mantik Penusa Vol. 3 No. 2,Des (2019): Manajemen Dan Informatika
Publisher : Lembaga Penelitian dan Pengabdian (LPPM) STMIK Pelita Nusantara Medan

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Tanah memiliki kandungan unsur kimia, fisika maupun biologi. Setiap tanaman memiliki type tanah yang bisa dia tumbuh dengan baik. Contohnya tanaman padi, padi akan tumbuh dengan baik juka Kebutuhan N optimum : 300kg/ha Urea, P optimum : 50 kg/ha SP36, K optimum : 50 kg/ha KCl Untuk itu perlu seorang petani untuk mengetahui unsur tersebut sebelum menanam tanaman. Mengacu pada hal itu membuat penulis mengambil tema hal tersebut dengan judul Pengembangan Perangkat Cerdas Untuk Deteksi Unsur Kandungan Tanah Dalam Menentukan Jenis Tanaman Mendukung Ketahanan Pangan. Diagnosis terhadap kandungan tanah dapat lebih jelas dengan menggunakan uji laboratorium dilanjut ke uji sensor Warna, Bau, dan Suhu untuk menentukan warna,bau dan suhu dari tiap kandungan unsur tanah yang di uji di laboratorium yang diproses dengan ilmu pengolahan citra. Setelah itu akan dirancang sembuah algoritma untuk mengkolaborasikan hasil ketiga sensor tesebut agar menjadi sebuat keputusan. Fitur spesifik yang telah diperoleh dapat dijadikan data pokok pada data mining sehingga nantinya sistem mampu menentukan jenis tanaman. Sehingga risiko kesalahan penentuan jenis tanaman diminimalisasi karena dapat ditentukan secara akurat berdasarkan fitur-fitur spesifik dari tanah yang diperoleh dengan bantuan komputer.
Implementation Of Data Mining In Determining Sales Pattern Of Snack Products Using Apriori Algorithm (Case Study: PT Siantar Top Tbk) Siti Patimah; Penda Sudarto Hasugian
Journal of Intelligent Decision Support System (IDSS) Vol 3 No 4 (2020): December: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

PT Siantar Top.Tbk is a company engaged in snack manufacturing which is located at Jl Raya Medan Tebing Tinggi, Ujung Serdang, Tanjung Morawa, Deli Serdang Regency, North Sumatra. In the sales transaction data processing process at PT Siantar Top Tbk, it has not been able to provide accurate information about the pattern or relationship of a set of items purchased by customers. So that the company has difficulty knowing every product that is sold, because the sales data is always increasing, but the company does not understand how to manage the sales data of these snack products. Because the snack product sales data is only archived and not managed by the company to get new results. The purpose of this research is to design and build a priori algorithm in determining sales patterns. This system is designed using UML and is built with the programming languages ​​PHP, HTML, CSS, Javascript and Mysql as the database. Then the determination of the sales pattern of snack products that are successful every month at PT Siantar Top Tbk using the Apriori algorithm. A priori algorithm is a data mining technique to find associative rules between a combination of items.
Hybrid System for Palm Line Detection and Educational Health Prediction Using Certainty Factor Method Erwin Panggabean; Wira Apriani; Nuraisana, Nuraisana; Penda Sudarto Hasugian
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 4 No. 06 (2025): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), July 2025
Publisher : Sean Institute

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Abstract

The difficulty in understanding individual characteristics based on palm lines is still an attraction in the context of education and technology-based experiments. This study aims to develop an educational application that is able to detect palm lines using a laptop camera, then predict certain characters or conditions based on the input. This system is built using the Certainty Factor (CF) method to provide certainty-based inferences on the visual symptoms of the detected palm lines. The process begins with taking a picture of the hand directly through the camera, followed by detection of main lines such as the life line, head line, and heart line using simple image processing techniques. After that, the system will display symptom-based questions related to the shape of the visible palm lines, then calculate the certainty value of the inference results using CF. This application is non-commercial and was developed as an educational tool to introduce the basic concepts of expert systems and Python-based visual processing. The system has successfully detected major palm lines with an accuracy of 80% under standard lighting conditions, and produced predictive results with certainty values that matched expected outcomes in over 70% of test cases. This demonstrates the potential of the CF method in processing visual data for educational inference. The system functions reliably as an educational tool, successfully demonstrating how certainty-based logic can be applied to simple visual data, and has been well-received in testing scenarios for learning purposes.
Co-Authors Asay Kumar Efrika Manurung Erwin Panggabean Kelvin Kalsel Nuraisana, Nuraisana Relima Mahdalena Simanjorang Rohit Siti Patimah Wira Apriani