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Penerapan Algoritma C4.5 dalam Klasifikasi Prestasi Atlet: Studi Kasus pada Daftar Nama Penerima Penghargaan Tahun 2023 Andi Diah Kuswanto; Hotman Nicolas Badjo; Septian Kharist; Muhammad Zayyid Mubarok; Riski Saputra; Rivaldi Muhamad Fitroh
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 2 No. 3 (2024): Juli : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v2i3.110

This study aims to apply the C4.5 algorithm in classifying athlete performance based on the 2023 award recipient list. The C4.5 algorithm was chosen for its ability to construct decision trees that can identify patterns and characteristics distinguishing high-performing athletes. The data used in this study includes various attributes such as gender, age, sport, number of medals, and level of competition participation. The results show that the C4.5 algorithm can classify athletes with high accuracy. The resulting decision tree provides valuable insights into the key factors contributing to athlete performance. The implementation of this algorithm is expected to assist sports organizations in more effectively identifying and developing potential talents.

Penerapan K-Means Clustering Untuk Menentukan Jumlah Pengangguran Berdasarkan Umur : Studi Kasus Di Badan Statistik Provinsi DKI Jakarta 2020-2022 Andi Diah Kuswanto; Azumardi Nabil Fadhila; Paulus Tri Setiawan; Muhammad Kevin Setiawan; Dody Renal Syahputra
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 2 No. 3 (2024): Juli : Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v2i3.116

Unemployment is a persistent problem in the labor market, thus hampering economic development and national prosperity. Indonesia, including its capital Jakarta, continues to face significant levels of unemployment compared to neighboring countries. This research focuses on analyzing the structure of unemployment in Jakarta using K-Means Clustering to categorize unemployment data based on age groups (2020-2022) sourced from the Central Statistics Agency. Analysis carried out via RapidMiner revealed three clusters:-Cluster 0: Age 30-60 years and above, Cluster 1: Age 20-24 years, Cluster 2: Age 15-19 and 25-29 years. The findings show that the 20-24 year age group has the highest unemployment rate (399,167 people), while the 30-60 year and above age group shows the lowest unemployment rate (75,560 people). This clustering approach provides insight into the distribution of unemployment by different age demographics in Jakarta, highlighting areas where targeted interventions may be needed to effectively address this socio-economic challenge

Analisa Data Shopping Trends Menggunakan Algoritma Klasifikasi Dengan Metode Naive Bayes Andi Diah Kuswanto; Said Imam Puro; Jodi Hariyan; Ridho Rafliansyah; Muhammad Rival Aziz; Pebro Vaulina Rajagukguk
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 2 No. 3 (2024): Juli : Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v2i3.118

In the era of rapid digitalization, understanding consumer behavior through data is becoming increasingly important for retail businesses. Shopping trends, such as those contained in this study, provide in-depth insights into various aspects of consumer behavior, from demographics to purchasing preferences and patterns of discount usage. This data is invaluable in formulating effective marketing strategies, improving customer experience, and optimizing business operations. The data used in this study included a variety of relevant variables, such as age, gender, location, product categories purchased, number of purchases, payment methods, and frequency of purchases. This information allows for a comprehensive analysis of how these factors affect consumer spending decisions. For example, analytics can reveal seasonal trends in purchases, product color and size preferences, and the impact of discounts and promo codes on sales volume. In addition, this dataset also reflects the changes in consumer behavior that have occurred over the past few years. Quantitative methodology is a research approach used to collect and analyze numerical data to understand patterns, relationships, and events in a given population. Data is collected from various sources such as online sales transactions, consumer surveys, Naive Bayesian algorithms are applied to the dataset that has been processed. The data was divided into two sets: training (80%) and testing (20%).

Penerapan Algoritma Linear Regression Dalam Memprediksi Harga Saham Bank BRI Andi Diah Kuswanto; Auliya Putri Amanda; Yoseba Priscilla; Maranatha Magdalena; Ananti Putri Safira; Izza Maulida
Switch : Jurnal Sains dan Teknologi Informasi Vol. 2 No. 4 (2024): Juli : Switch: Jurnal Sains dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/switch.v2i4.119

Investment is the act of placing funds in the hope of getting additional money or profits. Basically, investing involves placing a certain amount of funds today in the hope of making a profit in the future. From this understanding, it can be concluded that stock investment is the allocation of current sources of funds with the hope of gaining profits in the future through purchasing securities in the form of shares. The aim is to obtain additional or certain profits from the funds invested in trading shares on the stock exchange.

Penerapan Algoritma Apriori Dalam Analisis Keranjang Belanja Retail Di Wilayah Jawa Barat Andi Diah Kuswanto; Achmad Rizqullah Blessar; Abdul Goni; Arya Nibras Nayottama Sidiki; Oke Rizki Abdullah Haryu; Hafid Anhar Hamiki
Saturnus : Jurnal Teknologi dan Sistem Informasi Vol. 2 No. 3 (2024): Juli : Saturnus : Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v2i3.208

Market basket analysis is an important technique in data mining used to understand consumer purchasing patterns. This research uses the Apriori algorithm to identify relationships between products in the shopping basket, aiming to improve sales and marketing strategies in the retail industry. The focus of this study is on retail transaction data from West Java Province, which has a large and diverse population, reflecting complex consumer purchasing patterns. The research identifies several key issues: limited understanding of consumer behavior, unoptimized business strategy opportunities, and challenges in managing large transaction data. As a solution, the application of the Apriori algorithm can help find frequent consumer purchasing patterns and design more effective marketing strategies. The results show that market basket analysis using the Apriori algorithm is effective in understanding consumer purchasing patterns in the retail industry. This algorithm allows companies to discover itemsets that frequently appear together in transactions, which can be used to design more effective marketing and sales strategies.

Penerapan Algoritma C4.5 Dalam Klasifikasi Prestasi Atlet: Studi Kasus Pada Daftar Nama Penerima Penghargaan Tahun 2023 Andi Diah Kuswanto; Hotman Nicolas Badjo; Septian Kharist; Muhammad Zayyid Mubarok; Riski Saputra; Rivaldi Muhamad Fitroh
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 2 No. 3 (2024): Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v2i3.115

This study aims to apply the C4.5 algorithm in classifying athlete performance based on the 2023 award recipient list. The C4.5 algorithm was chosen for its ability to construct decision trees that can identify patterns and characteristics distinguishing high-performing athletes. The data used in this study includes various attributes such as gender, age, sport, number of medals, and level of competition participation. The results show that the C4.5 algorithm can classify athletes with high accuracy. The resulting decision tree provides valuable insights into the key factors contributing to athlete performance. The implementation of this algorithm is expected to assist sports organizations in more effectively identifying and developing potential talents.

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