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All Journal Prosiding SNATIF Infotech Journal Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL ILMIAH MATEMATIKA DAN TERAPAN Jiko (Jurnal Informatika dan komputer) JURIKOM (Jurnal Riset Komputer) Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Journal La Multiapp Jurnal Locus Penelitian dan Pengabdian Jurnal Algoritma Enrichment: Journal of Multidisciplinary Research and Development Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Jurnal Informatika Polinema (JIP) Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Gunawan Abdillah, Gunawan
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Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Social Sciences Transportation

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Product Layout Determination System Using the Association Rules Method Using the Equivalence Class Transformation Algorithm Haikal, Ahmed; Chrisnanto, Yulison Herry; Abdillah, Gunawan
Enrichment: Journal of Multidisciplinary Research and Development Vol. 1 No. 6 (2023): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v1i6.52

Abstract

Competition in the business world, specifically in the sales industry, requires companies to analyze the purchases made by customers during transactions in order to find effective business strategies. In the competitive fashion industry, merchants devise marketing strategies to increase sales. One strategy that can attract consumer interest is by organizing and arranging product displays, placing them in perfect layouts that align with customers' buying habits, making it easier for them to find and purchase products. Layout arrangement significantly influences customer satisfaction and purchase intent. The algorithm used in this study is Equivalence Class Transformation (ECLAT). The data used consists of transactional data from Aufco Clothing, specifically fashion products. A total of 1041 transactions were analyzed, using variables such as order number and items sold. The data was processed using JavaScript, with a minimum support of 0.2 and a minimum confidence of 0.7, resulting in 16 rules. The rules ranged from a min. confidence of 70% to a maximum confidence of 100%, forming 6 rules with 9 combinations of items.
Identification of Hoax News in the Using Community TF-RF and C5.0 Tree Decision Algorithm Santoso, Enrico Budi; Chrisnanto, Yulison Herry; Abdillah, Gunawan
Enrichment: Journal of Multidisciplinary Research and Development Vol. 1 No. 6 (2023): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v1i6.58

Abstract

News has a great influence on social and political conditions, and news can drive the economy of a country. Identifying hoax news is very important to ensure that the information circulating in society is true and reliable, and helps limit the spread of false information. In the process of reading news spread on social media, people do not know whether it is fact or hoax news because they cannot distinguish whether the news circulating is real news or fake news which if left unchecked can result in the public being misinformed. Therefore, this research process is to create a sistem for identifying hoax news using Decision Tree C5.0, which is an algorithm for the development of the C4.5 algorithm which in a process is almost similar, but the C5.0 algorithm has more value than the C4.5 algorithm which is used for the data mining process with a classification method for 1000 data obtained by web scraping using the keywords "election 2024", "politics" and "checkfaktapilkadamafindo" on the Turnbackhoax.id and Detik.com sites. In this study, what distinguishes it from several previous studies is its existence in several test scenarios, namely classification using feature weighting, which in classification using feature weighting is TF.RF. After testing the confusion matrix on the C5.0 algorithm, it produces accuracy, precision, and recall on each training / test data (70/30) resulting in accuracy 79.33%, precision 80.00%, recall 97.00%, then training / test data (80/20) resulting in accruracy 79.50%, precision 81.00%, recall 95.00%, then training and test data (90/10) resulting in accuracy 72.00%, precision 74.00%, recall 89.00%.
Consumer segmentation using K-Medians algorithm on transaction data based on LRFMP (length, recency, frequency, monetary, periodecity) Maulana, Akbar Dena; Ningsih, Ade Kania; Abdillah, Gunawan
Enrichment: Journal of Multidisciplinary Research and Development Vol. 1 No. 8 (2023): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v1i8.70

Abstract

Consumer loyalty has a crucial role for companies, especially in conditions of competition between companies. Success in retaining loyal customers is crucial. For this reason, customer loyalty analysis is needed to identify the level of consumer compliance with the company. In this case, consumer segmentation is also an important step to group consumers with similar characteristics to facilitate the management process. One of the analysis methods used is the LRFMP (Length, Recency, Frequency, Monetary, Periodecity) model, which examines consumer purchasing patterns based on various factors such as relationship length, last transaction time span, number of transactions, total money spent, and purchase regularity. The K-Medians grouping method was also used in this study. The data used is the history of purchase transactions in e-commerce for 373 days. From the application of LRFMP analysis and the K-Medians method, 4 clusters were obtained. The number of consumers in cluster 1 is 1183, cluster 2 is 1221, cluster 3 is 1206, and cluster 4 is 1102. The interpretation of the LRFMP model shows that 25.1% of consumers have high loyalty potential, 25.9% of consumers have low loyalty potential, 25.6% of consumers have high loyalty potential, and 23.4% of consumers have medium loyalty potential.
Optimasi Prediksi Penjualan Retail Online Menggunakan LightGBM dan Hyperparameter Tuning Nailendra, Septian Yudha; Witanti, Wina; Abdillah, Gunawan
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2551

Abstract

This research aims to develop and optimize a daily sales prediction model based on time series data using the Light Gradient Boosting Machine (LightGBM) algorithm on online retail data from the Olist marketplace. The research process began with merging and aggregating e-commerce transaction data into a daily format, followed by outlier handling using the Interquartile Range (IQR) capping method, and feature engineering to add temporal and historical information such as prev_day_sales and day_of_week. The dataset was then split into training and testing sets using a time-based split approach. A baseline model was trained with default parameters and subsequently optimized through hyperparameter tuning using GridSearchCV with TimeSeriesSplit cross-validation. Evaluation was conducted using MAE, RMSE, and R² metrics. The results show that the tuned model improved prediction accuracy, with MAE reduced by 5.34%, RMSE decreased by 8.34%, and R² increased by 0.76%. The one-day-ahead daily sales prediction reached R$ 1,676.86 and closely followed the actual sales pattern. This study demonstrates that a systematic approach involving data preprocessing, feature engineering, and parameter tuning can produce a more accurate, stable, and practical sales prediction model to support decision-making in the e-commerce sector. Theoretically, this research contributes to strengthening the understanding of the effectiveness of the LightGBM algorithm in daily time series modeling, particularly through the integration of temporal feature engineering and systematic parameter tuning strategies. These findings underscore the importance of a comprehensive approach in building accurate sales prediction models.
Sistem Pendukung Keputusan Penentuan Calon Transmigran Menggunakan Simple Additive Weighting dan Profile Matching Wardani, Mathilda Fitri; Abdillah, Gunawan; Komarudin, Agus
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 3 No. 1 (2019): PROSIDING SEMNAS INOTEK Ke-III Tahun 2019
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/inotek.v3i1.520

Abstract

Transmigrasi merupakan salah satu program pemerintah dalam mengatasi masalah kepadatan penduduk dengan memindahkan penduduk dari suatu daerah yang padat penduduk ke daerah lain yang jarang penduduk di wilayah Indonesia berlandaskan pada Undang-Undang Nomor 25 tahun 2000 dan Peraturan Pemerintah Nomor 2 Tahun 1999 tentang Penyelenggaraan Transmigrasi. Calon transmigran tidak dapat menentukan sendiri daerah tujuan transmigrasi melainkan harus disesuaikan dengan peraturan yang telah ditetapkan oleh pemerintah, sehingga pemerintah diharuskan untuk dapat memilih daerah tujuan transmigrasi yang tepat sasaran untuk calon transmigran. Penelitian ini telah membangun sistem yang mampu merekomendasikan prioritas calon transmigran beserta daerah tujuan transmigrasi dari segi usia, jumlah anggota keluarga, pekerjaan, pendidikan dan keterampilan dengan bobot yang ditentukan oleh pengguna. Metode yang digunakan adalah Simple Additive Weighting dan Profile Matching. Hasil dari uji coba sistem ini menghasilkan presentase akurasi sistem untuk penentuan calon transmigran sebesar 50% dan akurasi sistem untuk penentuan daerah tujuan transmigrasi sebesar 90%.
Co-Authors Ade Kania Ningsih, Ade Kania Al Farisi, Muhammad Farid Andri Taufiq, Andri Anggoro, Sigit Ashaury, Herdi Atamimi, Fadel Muhamad Hafid Azzahra, Cynthia Nur Chrisnanto, Yulison Harry Christanto, Yulison Herry Darmawan, Raja Dewi Purnamasari Edvin Ramadhan Eriyadi, Maulidina Norick Fadilah, Rifal Faiza Renaldi, Faiza Febiyane, Raisya Firdaus, Muhammad Fauzi Ghozali, Mohamad Aditya Muttaqin Hadiana, Asep Id Haikal, Ahmed Hasbia, Muhamad Komarudin, Agus Komarudin, Agus Kurniawan, Muhammad Randy Maulana, Akbar Dena Nailendra, Septian Yudha Rahayu, Ania Ramadhani, Hary Rizqi Ridwan Ilyas Riyadi, Saiful Faris Rusmana, Hendri Diana Santoso, Enrico Budi Wahyudiputra, Helmy Fauzan Wardani, Mathilda Fitri Wina Witanti Yulison Herry Chrisnanto Zizilia, Regitha