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All Journal Teknika Brilliance: Research of Artificial Intelligence TECHSI - Jurnal Teknik Informatika Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Hafizh Al Kautsar Aidilof
Malikussaleh University
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Other

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Decision Support System For Selecting School Majors In Man Using The Weighted Aggregated Sum Product Assessment Method Rifky Firzani Marpuang; Wahyu Fuadi; Hafizh Al Kautsar Aidilof
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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

Abstract

Determining school majors at Madrasah Aliyah Negeri (MAN) is an important process that must consider various factors such as academic grades, interests, and talents of students. However, the determination of the applicable majors still has problems due to several factors such as the environment or parents' requests so that the selection of students' majors is not in accordance with the abilities and talents of students. The selection of majors is also still done manually, so it is less efficient and takes a long time. This research aims to design and build a web-based Decision Support System (DSS) using the Weighted Aggregated Sum Product Assessment (WASPAS) method that is useful for helping schools determine majors that are in accordance with student abilities. This decision support system combines various criteria such as report card scores, academic test results, and practical skills to provide optimal majoring recommendations. The WASPAS method was chosen because of its ability to combine the Weighted Sum Model (WSM) and Weighted Product Model (WPM) methods, which results in more accurate calculations. The calculation test results show that the system built is able to provide major recommendations more quickly and effectively compared to the manual method. This system is expected to help schools in managing student majors more efficiently and objectively.
YOLOv8-Based Multi-Class Detection of Coffee Bean Defects and Contaminants for Automated Quality Grading Sayid Muhammad Jundullah; Hafizh Al Kautsar Aidilof; Fadlisyah
Brilliance: Research of Artificial Intelligence Vol. 6 No. 2 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i2.8612

Abstract

The quality of coffee beans is a crucial factor in determining export value and compliance with international standards set by the International Coffee Organization (ICO) and Standar Nasional Indonesia (SNI). Traditional manual sorting methods are time-consuming, labor-intensive, and prone to human subjectivity and inconsistency. This study aims to develop an automated coffee bean quality grading system using the YOLOv8s object detection model to accurately identify 20 classes of physical defects and contaminants from static images and automatically calculate the quality grade. A dataset consisting of 2,000 annotated images of Arabica and Robusta coffee beans was collected and divided into training (70%), validation (20%), and testing (10%) sets. The YOLOv8s model was trained using transfer learning with pre-trained weights and data augmentation techniques, then integrated into a web-based application using FastAPI for defect detection and automated defect scoring based on ICO and SNI 01-2907-2008 standards. Experimental results showed that the proposed model achieved a mean Average Precision (mAP@0.5) of 0.75, precision of 0.76, and recall of 0.75. The model performed excellently on distinct classes such as normal beans, large husk fragments, stones, and twigs, while facing challenges in differentiating visually similar defects like variants of black beans and sour beans. This study demonstrates the effectiveness of YOLOv8s for multi-class coffee bean defect detection and provides a practical, scalable, and objective solution for coffee quality assessment, significantly reducing reliance on manual inspection while improving consistency and efficiency in the grading process.
ANALISIS FUNDAMENTAL DALAM MEMILIH ALTCOIN PADA CRYPTOCURRENCY DENGAN PREFERENCE SELECTION INDEX (PSI) METHOD Huan Margana Ritonga; Zara Yunizar; Hafizh Al Kautsar Aidilof
TECHSI - Jurnal Teknik Informatika Vol. 15 No. 2 (2024)
Publisher : Teknik Informatika Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/techsi.v15i2.19000

Abstract

Cryptocurrency telah menjadi salah satu topik yang menarik perhatian di dunia keuangan dan teknologi dalam beberapa tahun terakhir. Seiring dengan popularitas Bitcoin, munculnya altcoin (alternative coins) juga menunjukkan potensi besar dalam pasar cryptocurrency. Skripsi ini bertujuan untuk menentukan opsi altcoin dengan investasi paling bagus dan memiliki potensi kenaikan paling tinggi. Metode Preference Selection Index adalah metode yang paling tepat dan di pilih untuk kasus ini karena didasari dengan beberapa preferensi aalternatif dan juga kriteria yang mendukung.Web yang dikembangkan memungkinkan pengguna untuk jauh lebih mudah memilih altcoin paling berpotensi dari beberapa opsi yang telah di pilih.
Implementasi Algoritma K-Medoid pada Clustering Sayuran Unggulan di Kabupaten Aceh Utara Rini Meiyanti; Muhammad Muaz Munauwar; Rahma Fitria; Hafizh Al Kautsar Aidilof
TEKNIKA Vol. 19 No. 1 (2025): TEKNIKA
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14378546

Abstract

Klasterisasi tanaman pada kelompok tani adalah proses pengelompokan tanaman berdasarkan karakteristik tertentu, seperti jenis tanaman, musim tanam, atau lokasi geografis, dengan tujuan meningkatkan efisiensi produksi. Kelompok Tani KWT Meugah Raya masih belum mampu melebihi hasil produksi pertanian sayuran di Aceh Utara. Metode data mining dapat mengidentifikasi pola-pola menarik dalam kumpulan data, salah satunya adalah algoritma K-Medoids clustering yang mengelompokkan data berdasarkan kesamaan karakteristik. Cluster terbentuk dengan menghitung sejauh mana kedekatan antara medoid dan objek non-medoids. Data yang digunakan adalah data dari Badan Pusat Statistik (BPS) pada tahun 2021-2023 di Kabupaten Aceh Utara mengumpulkan data dari 5 kategori sayuran dan 4 variabel, meliputi luas panen, produksi, luas tanaman, dan luas penanaman baru. Melalui algoritma K-Medoids, hasil klastering sayuran unggulan menghasilkan pengelompokan potensi ke dalam 3 klaster, yaitu klaster rendah (C1), sedang (C2), dan tinggi (C3) dengan mengumpulkan macam-macam data sayuran yang ditanam oleh masyarakat setempat berupa cabai besar, kacang panjang, kangkung, terong dan tomat. Langkah berikutnya adalah Menentukan nilai titik pusat awal dengan menyusun data berdasarkan urutan dari yang terendah hingga tertinggi pada setiap data variabel, berdasarkan keseluruhan data yang tersedia. Berdasarkan hasil penelitian ini, metode K-Medoids terbukti sangat efektif dalam melakukan clustering pada data hasil panen tanaman hortikultura. Evaluasi kinerja algoritma dilakukan dengan memanfaatkan Davies-Bouldin Index (DBI) sebagai metode evaluasi, dilakukan pengukuran untuk menilai kualitas pengelompokan yang dihasilkan. Setelah proses evaluasi menggunakan DBI selesai, algoritma K-Medoids memperoleh nilai 0,5537744324187953.
Co-Authors Fadlisyah Huan Margana Ritonga Muhammad Muaz Munauwar Rahma Fitria Rifky Firzani Marpuang Rini Meiyanti Sayid Muhammad Jundullah Wahyu Fuadi Zara Yunizar