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All Journal Jurnal Teknologi Informasi dan Multimedia JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
Fajriyanto
Universitas Ibrahimy Situbondo
Computer Science & IT Control & Systems Engineering Engineering

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Implementasi Algoritma Apriori untuk Menentukan Pola Transaksi Penjualan Berbasis Web Muhammad Ali Ridla; Fajriyanto; Misbahul Marzuqi
Jurnal Teknologi Informasi dan Multimedia Vol. 5 No. 3 (2023): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i3.399

Abstract

The Apriori algorithm is an algorithm that is well known for searching frequent itemsets using the association rule technique. The calculation of the Apriori algorithm uses minimal support and minimal confidence to determine the limit for calculating goods. The a priori algorithm functions to determine the pattern of sales of goods that are often purchased together by customers. The history of sales transactions owned by a store can be calculated for its frequent itemset pattern by using an a priori algorithm so that customers can find patterns of items that are often purchased simultaneously by customers. Therefore, the a priori algorithm is very important to be used by shop owners because it can determine sales strategies and the placement of goods that are often purchased simultaneously by customers. In this study, the authors succeeded in calculating a sales transaction by determining a minimum support limit of 10% and a minimum confidence of 10%. With the minimum support and minimum confidence that has been set by the author to see the results of the a priori algorithm for sales, then the results of 2 combinations of itemsets that meet the calculation requirements are obtained.
Student Graduation Prediction at Ibrahimy University Using K-Nearest Neighbor (KNN) Algorithm Herlinatus Safira Muasolli; Achmad Baijuri; Fajriyanto
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 2 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i2.5567

Abstract

Student graduation is an important indicator of a university's success in delivering quality education. Ibrahimy University faces challenges in objectively and proactively predicting student graduation, as academic evaluation processes remain conventional and reactive. This study aims to build a student graduation prediction system using the K-Nearest Neighbor (KNN) algorithm based on academic data including GPA, credits, attendance, and number of failed courses. The dataset consists of 150 student records from Ibrahimy University, developed using the Knowledge Discovery in Database (KDD) framework. Data was split into 80% training and 20% testing with StandardScaler normalization. The optimal k value was searched from k=1 to k=15. Results show that k=1 achieved the highest accuracy of 96.67%. The system is deployed as an interactive web application using Streamlit, enabling non-technical users such as lecturers and academic administrators to monitor student graduation potential more effectively and data-driven.
Co-Authors Achmad Baijuri Herlinatus Safira Muasolli Misbahul Marzuqi Muhammad Ali Ridla