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All Journal Jurnal Sains Dan Teknologi (SAINTEKBU) ABIDUMASY : Jurnal Pengabdian kepada Masyarakat Jumat Informatika: Jurnal Pengabdian Masyarakat Jumat Keagamaan: Jurnal Pengabdian Masyarakat Newton : Networking And Information Technology
Miftachuddin, Achmad Agus Athok
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Journal : Newton : Networking And Information Technology

 1 
Implementation of Apriori and Fp-Growth Algorithms In Forming Association Patterns Based On Unwaha Cooperative Sales Transactions Cahyaningtyas, Dhita; Miftachuddin, Achmad Agus Athok
NEWTON: Networking and Information Technology Vol. 4 No. 2 (2024): October
Publisher : LPPM Universitas KH. A. Wahab Hasbullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/newton.v4i2.5085

Abstract

This study implements the Apriori and FP-Growth algorithms to identify association rules in sales transaction data from the UNWAHA Multi-Purpose Cooperative. Both algorithms successfully discovered product relationships, with similarities in rules for items like MAKARONI ASEP, KRUPUK PAK JONO, and KRUPUK 500. The FP-Growth algorithm, implemented using RapidMiner, outperformed Apriori in processing speed by 11 seconds and demonstrated higher accuracy in rule generation. Optimal results were achieved with minimum support and confidence values of 0.3 and 0.9 for Apriori (generating 5 rules), and 0.52 and 0.9 for FP-Growth (generating 6 rules). These settings balanced between generating too many rules, which could complicate interpretation, and too few, which might miss important patterns. Based on the analysis, strategic recommendations for the cooperative include implementing product bundling and discounts for frequently co-purchased items nearing expiration, optimizing product placement by grouping commonly associated items (e.g., MAKARONI ASEP, KRUPUK PAK JONO, KRUPUK 500, SOSIS SO NICE, and YUPI ALL VARIAN 5G) in easily accessible locations, and increasing stock for high-demand products like LE MINERALE. This research demonstrates the practical application of association rule mining in retail, offering data-driven insights to enhance sales strategies and inventory management for the UNWAHA Cooperative.
Implementation of Apriori and Fp-Growth Algorithms In Forming Association Patterns Based On Unwaha Cooperative Sales Transactions Cahyaningtyas, Dhita; Miftachuddin, Achmad Agus Athok
NEWTON: Networking and Information Technology Vol. 4 No. 2 (2024): October
Publisher : LPPM Universitas KH. A. Wahab Hasbullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/newton.v4i2.5085

Abstract

This study implements the Apriori and FP-Growth algorithms to identify association rules in sales transaction data from the UNWAHA Multi-Purpose Cooperative. Both algorithms successfully discovered product relationships, with similarities in rules for items like MAKARONI ASEP, KRUPUK PAK JONO, and KRUPUK 500. The FP-Growth algorithm, implemented using RapidMiner, outperformed Apriori in processing speed by 11 seconds and demonstrated higher accuracy in rule generation. Optimal results were achieved with minimum support and confidence values of 0.3 and 0.9 for Apriori (generating 5 rules), and 0.52 and 0.9 for FP-Growth (generating 6 rules). These settings balanced between generating too many rules, which could complicate interpretation, and too few, which might miss important patterns. Based on the analysis, strategic recommendations for the cooperative include implementing product bundling and discounts for frequently co-purchased items nearing expiration, optimizing product placement by grouping commonly associated items (e.g., MAKARONI ASEP, KRUPUK PAK JONO, KRUPUK 500, SOSIS SO NICE, and YUPI ALL VARIAN 5G) in easily accessible locations, and increasing stock for high-demand products like LE MINERALE. This research demonstrates the practical application of association rule mining in retail, offering data-driven insights to enhance sales strategies and inventory management for the UNWAHA Cooperative.
Sentiment Analysis of Online Game Clash of Clans Reviews Using the K-Nearest Neighbor Method Prastyo, Andika; Miftachuddin, Achmad Agus Athok
NEWTON: Networking and Information Technology Vol. 4 No. 3 (2025): February
Publisher : LPPM Universitas KH. A. Wahab Hasbullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/newton.v4i3.5099

Abstract

Clash of Clans is a popular strategy game with millions of players worldwide. User reviews for this game are available on various online platforms. Sentiment analysis of these reviews can provide valuable insights into players' experiences and opinions. In this study, the researchers used the K-Nearest Neighbor (KNN) algorithm to classify the sentiment of Clash of Clans player reviews collected from the Google Play Store. Experimental results show that with a 60:40 training and testing data split, the KNN model was able to classify review sentiment with an accuracy of 64.52%, a precision value of 68.4%, a recall value of 88%, and an F1-score of 76.97%. The application of TF-IDF word weighting produced high accuracy at k-2 with an accuracy of 95.55%, precision of 96.16%, recall of 95.55%, and F1-score of 95.59%. These results indicate that KNN can be an efficient tool for analyzing player sentiment towards the Clash of Clans game.
Implementation of data mining to predict BLT receipts in Kedungbetik village using the c4.5 algorithm Saputra, Gilang Dwi; Miftachuddin, Achmad Agus Athok
NEWTON: Networking and Information Technology Vol. 4 No. 3 (2025): February
Publisher : LPPM Universitas KH. A. Wahab Hasbullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/newton.v4i3.5142

Abstract

Receiving Direct Cash Assistance (BLT) is a government program to help poor people meet basic needs. This research aims to implement data mining techniques to predict BLT receipts in Kedungbetik Village using the C4.5 algorithm. The C4.5 algorithm was chosen because of its ability to build efficient and accurate decision trees. The data used includes attributes such as ID, name, address, type of work, BLT criteria and class. The data is analyzed to find patterns and relationships that are relevant to the BLT acceptance criteria. The research results show that the C4.5 algorithm can build accurate prediction models with a high success rate. This model is expected to help village governments identify residents who are entitled to receive BLT in a more targeted manner. This research contributes to the development of a more transparent and accountable BLT recipient selection method, and can be applied in other villages with similar characteristics. In this way, it is hoped that the distribution of BLT will be more even and effective, helping to reduce the level of poverty in society.
Co-Authors Amrullah, M. Malik Fajar Anggi Indah Yuliana Astiti, Lidya Dwi Cahyaningtyas, Dhita Emha Taufiq Luthfi Fibrianti, Rindhi Dwi Firmansya, Yoga Fitra, Ziyyana Hardyanto, Mohamad David Wahyu Hardyanto Huda, Erfan Nur Imansyah, Bima Jalil, M. Abdul Kusrini, Kusrini Moh. Anshori Aris Widya Mushlih, Dani Huwaidi Prastyo, Andika Rahmadianti, Rizka Wulan Rohmat Hidayat Saputra, Gilang Dwi Sobirin, Asri Sujono Sujono, Sujono