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All Journal JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Nasional Ilmu Komputer Jurnal Pengembangan Sistem Informasi dan Informatika MDP Student Conference Jurnal Informatika dan Komputer
Prayogi, M. Bagus
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Library & Information Science Social Sciences

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Perbandingan Algoritma ID3, Naive Bayes, SVM Berbasis PSO untuk Prediksi Serangan Jantung Prayogi, M. Bagus; Irawan, Indra; Fajar, Yahya Ibnu
MDP Student Conference Vol 3 No 1 (2024): The 3rd MDP Student Conference 2024
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v3i1.6979

Abstract

This research aims to evaluate the precision of three primary predictive algorithms—ID3, naïve bayes, and SVM (SVM)—optimized using the particle swarm optimization (PSO) algorithm for detecting and predicting heart attacks. The methodology involves comparing these algorithms as tools for categorizing data into relevant groups and optimizing them using PSO to enhance prediction accuracy. Data from kaggle and uci repositories are managed using RapidMiner. The study reveals accuracy results: the ID3 algorithm achieves 75.20% accuracy with AUC 0.735, post-PSO optimization increases accuracy to 80.49% with AUC 0.815. The naïve bayes algorithm attains 81.52% accuracy with AUC 0.890, post-PSO optimization enhances accuracy to 83.94% with AUC 0.901. The SVM (SVM) algorithm records 82.13% accuracy with AUC 0.895, post-PSO optimization boosts accuracy to 84.83% with AUC 0.900.
Studi Perbandingan: Algoritma Random Forest, Naive Bayes Dan Support Vector Machine Dalam Analisis Sentimen Pada Aplikasi Capcut Di Google Play Store irawan, Indra; Wardianto, Wardianto; Wathan, M.Hizbul; Prayogi, M. Bagus
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 5 No. 4 (2024): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v5i4.1959

Abstract

CapCut, a highly popular video editing tool, boasts millions of users worldwide across various age groups. Posting reviews on the Google Play Store can provide valuable insights into this application. This study aims to evaluate the effectiveness of three classification algorithms Random Forest, Naïve Bayes, and Support Vector Machine in performing sentiment analysis on Google Play Store reviews of the CapCut application. User reviews are identified and categorized into positive, negative, and neutral labels using sentiment analysis methods. A total of three thousand user review datasets were employed in this investigation. The research procedure involved data preprocessing, feature extraction, and model training. The results show that the Random Forest classification method achieved 83% accuracy, the Naïve Bayes method 70% accuracy, and the Support Vector Machine method 86% accuracy, indicating user sentiment towards the CapCut application. With an accuracy of 0.86, the SVM algorithm is found to yield the best results
Analisis Sentimen Ulasan Pengguna Aplikasi Alfagift Menggunakan Random Forest Prayogi, M. Bagus; Masitoh, Gustina
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 2 (2025): May 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.2.158-170

Abstract

Alfagift is a mobile application developed by Alfamart to support online ordering, featuring promotions, transactions, ordering, and delivery from the nearest point based on the consumer’s address. User feedback on the Google Play Store reveals mixed sentiments, including both positive and negative responses, which developers can use as material to improve the application’s quality. This study focuses on assessing the sentiment of Alfagift app user reviews using the Random Forest algorithm. A total of 4,379 review data points were collected from the Google Play Store and grouped into two categories: positive and negative sentiment. The research steps include data collection, data labeling, data preprocessing, word weighting, dividing the data into training and testing sets, implementing the Random Forest algorithm, and model evaluation. The test results show that the Random Forest algorithm achieves an accuracy of 97.6% and an AUC of 0.98, which falls into the category of excellent classification. This research is expected to contribute to application developers’ understanding of user perceptions, enabling them to improve application quality and increase overall user convenience.
PERANCANGAN TOPOLOGI LAN PADA PERCETAKAN DARRA MENGGUNAKAN APLIKASI SISCO PACKET TRACER Prayogi, M. Bagus; Setia Dewi, Mita; anshori
JICode: Jurnal Informatika dan Komputer Vol. 1 No. 1 (2024): Edisi Februari
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30599/jicode.v1i1.3287

Abstract

As globalization progresses, technological developments are increasingly advanced, and human resource skills are needed in various fields. Computer network technology is proliferating because of the community's need for services using computer networks. This research was conducted to design a computer network using Cisco router switches at the Dara printing house, the owner of this printing house Aris Munandar, is located in BK4, kumpul Rejo Village, East Buay Madan District, East Ok, South Sumatra. This printing network has not been typologized LAN and only uses wireless via Wi-Fi. Another problem is that it only has 3 computers and 1 laptop, but only 1 computer and 1 laptop are connected to WiFi. The type of network designed refers to a local area network (LAN). The purpose of this network design is to design a LAN typology that facilitates data transfer and connectivity of multiple computers within a single local area. In addition, LAN networks can also improve employee efficiency and performance. This research was conducted using qualitative methods using direct observation in the field. The network development method used in this study is the NDLC (Network Development Life Cycle) method which consists of the stages of analysis, design, prototyping simulation, and management. After being developed through several stages, the results of this research were developed using Cisco Packet Tracer to analyze and design network designs.
PERANCANGAN SISTEM PENJUALAN BERBASIS DEKSTOP (STUDI KASUS : PERCETAKAN DARRA) Prayogi, M. Bagus; Fajar, Yahya Ibnu; Jakak , , Pamuji Muhamad
JICode: Jurnal Informatika dan Komputer Vol. 1 No. 2 (2024): Edisi Agustus
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30599/jicode.v1i2.3775

Abstract

Percetakan Darra menghadapi berbagai tantangan dalam mengelola penjualan dan persediaan secara efisien. Sistem manual yang digunakan saat ini sering menyebabkan ketidakakuratan data dan kesulitan dalam pemantauan stok barang. Oleh karena itu, diperlukan sebuah sistem informasi penjualan berbasis desktop untuk meningkatkan efisiensi dan akurasi dalam pengelolaan data penjualan. Penelitian ini bertujuan untuk merancang yang dapat digunakan untuk mengembangkan sistem informasi penjualan berbasis desktop yang dapat membantu Percetakan Darra dalam mengelola transaksi penjualan, persediaan barang, serta menghasilkan laporan yang akurat dan tepat waktu. Perancangan sistem menggunakan Unit Modeling Language (UML) sebagai alat bantu dalam proses perancangan sistem untuk memodelkan struktur dan perilaku sistem yang akan dibangun.
Prediksi Angka Harapan Hidup Menggunakan Random Forest dan XGBoost Regression Prayogi, M. Bagus; Fitria Apriani; Nirma
JICode: Jurnal Informatika dan Komputer Vol. 2 No. 1 (2025): Edisi Februari
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30599/ht1k1h53

Abstract

Angka harapan hidup mengacu pada estimasi rata-rata durasi kehidupan seseorang sejak kelahirannya. Indikator ini menjadi salah satu komponen penting dalam pengukuran indeks pembangunan manusia (IPM). Peningkatan harapan hidup biasanya berbanding lurus dengan kenaikan nilai IPM. Penelitian ini bertujuan untuk memprediksi tingkat harapan hidup menggunakan 2 algoritma regresi yaitu Random Forest regression dan XGBoost regression, serta menganalisis variabel yang paling berpengaruh terhadap harapan hidup. Dataset yang digunakan berasal dari Global Country Information Dataset 2023 yang tersedia di platform Kaggle. Berdasarkan hasil analisis, XGBoost regression terbukti memiliki performa terbaik dalam melakukan prediksi, sebagaimana ditunjukkan oleh nilai MAPE yang lebih rendah sebesar 2.60 dan R² yang lebih tinggi sebesar 90.53. Faktor-faktor seperti angka kematian bayi dan rasio kematian ibu ditemukan sebagai prediktor utama, sedangkan pengaruh Indeks Harga Konsumen (CPI) terhadap harapan hidup relatif lebih kecil.
Penerapan Metode K-Means Clustering Dalam Pengembangan Strategi Promosi Berbasis Data Penerimaan Mahasiswa Baru (Studi Kasus :Universitas Nurul Huda) indra, Indra Irawan; Rizki, Uli; Jakak, Pamuji M.; Prayogi, M. Bagus; Rahman, Miftakhul
Jurnal Nasional Ilmu Komputer Vol. 5 No. 1 (2024): Jurnal Nasional Ilmu Komputer
Publisher : Training and Research Institute Jeramba Ilmu Sukses (TRI - JIS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jurnalnik.v5i1.1656

Abstract

The admission of new students is a key element in the success of a university. In its effort to enhance efficiency in admitting new students, Nurul Huda University proposes applying the K-Means Clustering method as a solution to manage candidate student data more effectively and intelligently. The data used in this research is derived from the Admission of New Students process for the academic years 2021/2022 and 2022/2023, totaling 1275 entries, which will then be processed using data mining techniques to generate analysis. The goal is to determine promotional strategies based on the origin of profiles of newly enrolled students. The method applied is clustering with the K-Means algorithm. After data processing, analysis is conducted using the Knowledge Discovery in Databases (KDD) technique, consisting of five stages: selection, preprocessing, transformation, data mining, and evaluation. The implementation in this research utilizes Rapidminer software, resulting in three data clusters: Cluster 1 with 345 entries, covering 27% of the total; Cluster 2 with 86 entries (7%); and Cluster 3 with 835 entries (66%). For promotion, the marketing team is deployed to districts dominant in the East OKU region and potential areas outside the East OKU District. They conduct direct visits to introduce Nurul University to students, distribute brochures, display pamphlets, and adapt strategies using a promotion mix strategy
Co-Authors Anshori Fajar, Yahya Ibnu Fitria Apriani Indra Irawan Indra Irawan indra, Indra Irawan Jakak, Pamuji M. Masitoh, Gustina Nirma Pamuji Muhamad Jakak Rahman, Miftakhul Setia Dewi, Mita Wardianto, Wardianto Wathan, M.Hizbul