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All Journal Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika IT JOURNAL RESEARCH AND DEVELOPMENT ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Ilmu Komputer dan Informatika DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Jurnal Informatika: Jurnal Pengembangan IT
Gusrianty, Gusrianty
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Computer Science & IT Control & Systems Engineering Engineering

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ANALYSIS OF SLOW MOVING GOODS CLASSIFICATION TECHNIQUE: RANDOM FOREST AND NAïVE BAYES Jollyta, Deny; Gusrianty, Gusrianty; Sukrianto, Darmanta
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8263

Abstract

Classifications techniques in data mining are useful for grouping data based on the related criteria and history. Categorization of goods into slow moving group or the other is important because it affects the policy of the selling. Various classification algorithms are available to predict labels or class labels of data. Two of them are Random Forest and Naïve Bayes. Both algorithms have the ability to describe predictions in detail through indicators of accuracy, precision, and recall. This study aims to compare the performance of the two algorithms, which uses testing data of snacks with labels for package type, size, flavor and categories. The study attempts to analyze data patterns and decides whether or not the goods fall into the slow moving category. Our research shows that Random Forest algorithm predicts well with accuracy of 87.33%, precision of 85.82% and recall of 100%. The aforementioned algorithm performs better than Naïve Bayes algorithm which attains accuracy of 84.67%, precision of 88.33% and recall of 92.17%. Furthermore, Random Forest algorithm attains AUC value of 0.975 which is slightly higher than that attained by Naïve Bayes at 0.936. Random Forest algorithm is considered better based on the value of the metrics, which is reasonable because the algorithm does not produce bias and is very stable.
Penilaian Siswa menggunakan Metode Fuzzy Tsukamoto Prisilia, Vivi; Gusrianty, Gusrianty
Jurnal Ilmu Komputer dan Informatika Vol 3 No 2 (2023): JIKI - Desember 2023
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jiki.67

Abstract

Penilaian merupakan hal yang penting untuk tolak ukur keberhasilan seorang siswa, maka pengolahan data nilai nya juga menjadi hal yang penting dalam pengambilan sebuah keputusan dengan cepat dan tepat. Pengambil keputusan membutuhkan alternatif kriteria yang dapat digunakan dalam menentukan penilaian dalam pembelajaran matapelajaran IPA pada Kurikulum 2013 menggunakan metode fuzzy logic tsukomoto sesuai dengan kriteria yang diinginkan. Kriteria penilaian disesuaikan diambil dari  4 Kompetensi Inti yaitu KI-1 (Sikap Spriritual), KI-2 (Sikap Sosial), KI-3 (Pengetahuan) dan KI-4 (Keterampilan). Sebagai salah satu metode dalam pengambilan keputusan fuzzy logic tsukomoto dapat memberikan rekomendasi hasil yang lebih baik. Penelitian ini bertujuan untuk membantu dalam pengambilan keputusan penilaian pembelajaran siswa paa kurikulum 2013 menggunakan metode fuzzy logic. Hasil dari pengujian meperlihatkan bahwa metode fuzzy logic tsukomoto menghasilkan nilai 81,41 (Cukup) untuk output sikap dan 85,03 (Cukup Tuntas) untuk output prestasi.
N-gram and Kernel Performance Using Support Vector Machine Algorithm for Fake News Detection System Jollyta, Deny; Gusrianty, Gusrianty; Prihandoko, Prihandoko; Sukrianto, Darmanta
ILKOM Jurnal Ilmiah Vol 15, No 3 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i3.1770.398-404

Abstract

The modern technological advancements have made it simpler for fake news to circulate online. The researchers have developed several strategies to overcome this obstacle, including text classification, distribution network analysis, and human-machine hybrid methods. The most common method is text categorization, and many researchers offer deep learning and machine learning models as remedies. An Indonesian language fake news detection system based on news headlines was developed in this work using the Support Vector Machine (SVM) kernel and n-gram. The objective of this research is to identify the model that produces the best performance outcomes. The system deployment on the web will employ the model that produces the greatest outcomes. According to the research findings, the linear kernel SVM algorithm produces the best results, with an accuracy value of 0.974. Furthermore, the bigram feature used in the development of a classification model does not increase the precision of fake news identification in Indonesian. Utilizing the unigram function yields the most accurate results.
F Pencarian File Pada Arsip Karyawan Menggunakan Algoritma Sequential Search Melati, Anggun; Oktarina , Dwi; Gusrianty, Gusrianty; Joni Kurniawan, Wahyu
IT Journal Research and Development Vol. 7 No. 1 (2022)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2022.9210

Abstract

PT. Jaya Nika Permata Group is one of the companies engaged in the Bakery, Café and Restaurant which has been running for 37 years and is still active until now. Currently based in the city of Pekanbaru, and already has branches in Duri, Dumai, Batam. With the total number of employees of PT. Jaya Nika Permata Group has approximately 485 employees, of course, the number of employee files that will be archived is quite large and the file types are varied. In addition, the process of searching for employee files at the time of archived and when files are needed will take a long time considering the large number of employees. Currently, there is no system that can help facilitate the employee's file archiving activities. In this study, field research was conducted to determine the condition of the current file archiving system. Based on the analysis that has been obtained, research is carried out to develop an employee file filing system that is currently running at PT. Jaya Nika Permata. Searching files in employee archives using a sequential search algorithm is expected to facilitate employee file archiving activities and speed up the process of searching for employee files when needed.
Cluster Validity for Optimizing Classification Model: Davies Bouldin Index – Random Forest Algorithm Prihandoko, Prihandoko; Jollyta, Deny; Gusrianty, Gusrianty; Siddik, Muhammad; Johan, Johan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 24 No 1 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4043

Abstract

Several factors impact pregnant women’s health and mortality rates. The symptoms of disease in pregnant women are often similar. This makes it difficult to evaluate which factors contribute to a low, medium, or high risk of mortality among pregnant women. The purpose of this research is to generate classification rules for maternal health risk using optimal clusters. The optimal cluster is obtained from the process carried out by the validity cluster. The methods used are K-Means clustering, Davies Bouldin Index (DBI), and the Random Forest algorithm. These methods build optimum clusters from a set of k-tests to produce the best classification. Optimal clusters comprising cluster members withstrong similarities are high-dimensional data. Therefore, the Principal Component Analysis (PCA) technique is required to evaluate attribute value. The result of the research is that the best classification rule was obtained from k-tests = 22 on the 20th cluster, which has an accuracy of 97% to low, mid, and high risk. The novelty lies in using DBI for data that the Random Forest will classify. According to the research findings, the classification rules created through optimal clusters are 9.7% better than without the clustering process. This demonstrates that optimizing the data group has implications for enhancing the classification algorithm’s performance.
Pengembangan Aplikasi Media Pembelajaran Digital Berbasis Android untuk Pendidikan Pancasila Bagi Siswa Sekolah Dasar Valentino, Febri; Kurniawan, Wahyu Joni; Gusrianty, Gusrianty; Oktarina, Dwi
DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol 6, No 1: JUNI 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/device.v6i1.6364

Abstract

Mata pelajaran Pendidikan Pancasila di tingkat sekolah dasar memiliki peran penting dalam membentuk serta mengembangkan karakter peserta didik. Untuk mencapai hal tersebut, proses pembelajaran perlu dirancang secara terpadu dengan memperhatikan tujuan, isi materi, metode pengajaran, dan evaluasi, serta didukung oleh media dan strategi yang efektif. Sayangnya, metode pembelajaran yang digunakan saat ini masih didominasi oleh media tradisional yang monoton, sehingga berisiko menurunkan minat belajar siswa. Oleh karena itu, diperlukan inovasi melalui pengembangan media pembelajaran interaktif berbasis Android guna meningkatkan pemahaman dan antusiasme siswa dalam mengikuti pembelajaran. Penelitian ini menerapkan metode Multimedia Development Life Cycle (MDLC) untuk merancang dan mengembangkan aplikasi pembelajaran berbasis Android. Hasil penelitian menunjukkan bahwa media pembelajaran digital yang dikembangkan mampu meningkatkan pemahaman konsep dan motivasi belajar peserta didik secara efektif. 
Penerapan Linear Discriminant Analysis Untuk Meningkatkan Kinerja Algoritma Support Vector Machine Gusrianty, Gusrianty; Fenly, Fenly; Jollyta, Deny; Erlin, Erlin; Putri, Ramalia Noratama; Oktariana, Dwi
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.8772

Abstract

Obesity is a complex chronic disease influenced by various factors, such as genetic, environmental, and lifestyle, which is characterized by excess body weight due to the excessive accumulation of body fat. With the rapid advancement of technology and digitalization across all sectors, data has become increasingly vital, as large datasets generate valuable information. However, a key challenge in data analysis is addressing redundancy, noise, and high dimensionality, which can affect the performance of machine learning algorithms. This study aims to investigate the effectiveness of combining Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) in enhancing the accuracy and efficiency of high-dimensional data classification, particularly in predicting obesity levels. LDA is employed to reduce data dimensionality while retaining the most relevant features, whereas SVM is utilized as the classification algorithm to predict obesity levels based on patterns identified within the dataset. The research was conducted using a dataset consisting of 779 training samples and 195 testing samples. The results reveal that the combination of LDA and SVM achieved a classification accuracy of up to 99%, with a 50% reduction in data dimensionality and a computation speed of 0,0696 second. Moreover, computation time was significantly reduced, indicating that LDA not only facilitates data simplification but also improves the overall efficiency of the classification process.
Cluster Validity for Optimizing Classification Model: Davies Bouldin Index – Random Forest Algorithm Prihandoko, Prihandoko; Jollyta, Deny; Gusrianty, Gusrianty; Siddik, Muhammad; Johan, Johan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4043

Abstract

Several factors impact pregnant women’s health and mortality rates. The symptoms of disease in pregnant women are often similar. This makes it difficult to evaluate which factors contribute to a low, medium, or high risk of mortality among pregnant women. The purpose of this research is to generate classification rules for maternal health risk using optimal clusters. The optimal cluster is obtained from the process carried out by the validity cluster. The methods used are K-Means clustering, Davies Bouldin Index (DBI), and the Random Forest algorithm. These methods build optimum clusters from a set of k-tests to produce the best classification. Optimal clusters comprising cluster members withstrong similarities are high-dimensional data. Therefore, the Principal Component Analysis (PCA) technique is required to evaluate attribute value. The result of the research is that the best classification rule was obtained from k-tests = 22 on the 20th cluster, which has an accuracy of 97% to low, mid, and high risk. The novelty lies in using DBI for data that the Random Forest will classify. According to the research findings, the classification rules created through optimal clusters are 9.7% better than without the clustering process. This demonstrates that optimizing the data group has implications for enhancing the classification algorithm’s performance.
Co-Authors Deny Jollyta Dwi Oktarina Erlin Fenly, Fenly Johan Johan Joni Kurniawan, Wahyu Melati, Anggun Oktariana, Dwi Oktarina , Dwi Prihandoko Prihandoko Prisilia, Vivi Ramalia Noratama Putri Siddik, Muhammad Sukrianto, Darmanta Valentino, Febri Wahyu Joni Kurniawan, Wahyu Joni