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All Journal JOIV : International Journal on Informatics Visualization JURNAL TEKNIK INDUSTRI
Wahyu Adi, Prajanto
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Computer Science & IT Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering

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Optimasi Lokasi Distribution Center UKM Mina Indo Sejahtera dengan Model Matematis Haversin dan Centre of Gravity Adjusting Arsiwi, Pramudi; Wijaya, Dewa Kusuma; Wahyu Adi, Prajanto
JURNAL TEKNIK INDUSTRI Vol. 11 No. 3 (2021): VOLUME 11 NO 3 NOVEMBER 2021
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Indusri Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/jti.v11i3.13070

Abstract

Intisari— Biaya distribusi produk yang masih cukup tinggi dan juga lokasi konsumen yang tersebar di berbagai wilayah Pulau Jawa menjadi sebuah tantangan yang cukup kompleks bagi UKM Mina Indo Sejahtera, agar dapat selalu memenuhi mayoritas bahkan seluruh demand dari konsumen potensialnya dengan biaya yang seefisien mungkin. Oleh karena itu, diperlukan sebuah desain jaringan supply chain yang optimal bagi UKM Mina Indo Sejahtera, agar seluruh konsumen dapat tetap terlayani dengan total biaya distribusi yang tetap efisien. Metode yang digunakan yaitu dengan menggunakan formulasi model matematis haversin dan juga Center of Gravity (COG) Adjusting. Koordinat lokasi yang dihasilkan terbukti mampu meminimalkan total jarak tempuh dan juga total biaya yang harus dikeluarkan UKM Mina Indo Sejahtera untuk mendistribusikan produknya ke seluruh market khususnya di Pulau Jawa. Hasil penurunan total jarak tempuh dan total biaya distribusi bulanan yang didapatkan melalui formula haversin dan metode COG Adjusting yaitu sebesar 22% dan 24%. Sehingga, apabila UKM Mina Indo Sejahtera memusatkan distribusinya pada lokasi baru tersebut, total jarak yang ditempuh ke seluruh pasar di Pulau Jawa dapat diminimalkan menjadi sejauh 1.720, 4 kilometer, dengan total biaya distribusi bulanan yang dikeluarkan hanya sebesar Rp Rp 11.736.455,-.. Abstract— The cost of product distribution is still quite high and also the location of consumers spread across various regions of Java Island is a fairly complex challenge for Mina Indo Sejahtera SMEs, in order to always meet the majority and even all demands from potential consumers at the most efficient cost possible. Therefore, an optimal supply chain network design is needed for Mina Indo Sejahtera SMEs, so that all consumers can still be served with efficient total distribution costs. The method used is by using a haversin mathematical model formulation and also Center of Gravity (COG) Adjusting. The resulting location coordinates are proven to be able to minimize the total distance traveled and also the total costs that must be incurred by Mina Indo Sejahtera SMEs to distribute their products to all markets, especially in Java. The results of the reduction in total mileage and total monthly distribution costs obtained through the haversin formula and the COG Adjusting method are 22% and 24%, respectively. Thus, if Mina Indo Sejahtera SMEs concentrated their distribution in the new location, the total distance traveled to all markets on Java Island could be minimized to 1,720, 4 kilometers, with a total monthly distribution cost of only Rp. 11,736,455,-.
Optimizing Machine Learning Models for Anomaly-based IDS using Intercorrelation Threshold Wahyu Adi, Prajanto; Sugiharto, Aris; Malik Hakim, Muhammad; Rizki Saputra, Naufal; Hanif Setiawan, Syariful
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3355

Abstract

This study aims to improve the performance of attack detection on the Bot-IoT dataset that faces class imbalance. The method used involves developing a feature selection model based on the Pearson correlation coefficient between features, with an adaptive threshold applied. The datasets used consist of two types: D1, with the 10 best features, and D2, with all features. The oversampling technique is applied to the minority class, followed by calculating feature correlations to determine the best feature using a threshold based on the average of the highest and lowest correlations. The feature selection process is carried out iteratively, with performance testing across several machine learning algorithms, including KNN, Random Forest, Logistic Regression, and SVM. The results show that the proposed feature selection method can improve the performance of the minority class without sacrificing the majority class's performance. On the D1 dataset, the Random Forest algorithm achieved 96% accuracy, while KNN achieved 93%. On the D2 dataset, KNN achieved balanced performance, with average precision, recall, and F1-score of 0.99 for both classes, while Random Forest achieved lower results on the minority class. The implications of this study indicate that correlation-based feature selection can improve attack detection performance on datasets with high class imbalance, and it can be implemented in future studies to address similar problems in IoT-based intrusion detection systems.
Co-Authors Aris Sugiharto Hanif Setiawan, Syariful Muhammad Malik Hakim, Muhammad Malik Pramudi Arsiwi Rizki Saputra, Naufal Wijaya, Dewa Kusuma