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All Journal Jurnal Pendidikan Teknologi dan Kejuruan Voteteknika (Vocational Teknik Elektronika dan Informatika) Proceedings of KNASTIK Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Pekommas Jurnal Edukasi dan Penelitian Informatika (JEPIN) Infotech Journal Sistemasi: Jurnal Sistem Informasi Jurnal Sistem dan Manajemen Industri Jurnal Ilmiah Matrik BAREKENG: Jurnal Ilmu Matematika dan Terapan Matrix : Jurnal Manajemen Teknologi dan Informatika JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) INTECOMS: Journal of Information Technology and Computer Science KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) JUMANJI (Jurnal Masyarakat Informatika Unjani) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terpadu Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Teknik Informatika (JUTIF) Informatics and Digital Expert (INDEX) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Jurnal Sosial dan Teknologi Jurnal Locus Penelitian dan Pengabdian Jurnal Informatika Teknologi dan Sains (Jinteks) Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Algoritma Bincang Sains dan Teknologi J-Icon : Jurnal Komputer dan Informatika IJESPG (International Journal of Engineering, Economic, Social Politic and Government) journal Enrichment: Journal of Multidisciplinary Research and Development Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) RESLAJ: Religion Education Social Laa Roiba Journal In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Yulison Herry Chrisnanto
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Religion Aerospace Engineering Arts Humanities Automotive Engineering 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 Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Product Layout Determination System Using the Association Rules Method Using the Equivalence Class Transformation Algorithm Ahmed Haikal; Yulison Herry Chrisnanto; Gunawan Abdillah
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.
Covid-19 Sentiment Analysis Using Random Forest Classification Salsa Safira Nur Syamsi; Asep Id Hadiana; Yulison H. Chrisnanto
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.v1i6.53

Abstract

The spread of the COVID-19 pandemic has reached a significant global scale, changing the dynamics of people's lives around the world. Social media platforms such as Twitter have become important channels for individuals to share experiences, voice opinions, and participate in discussions related to this pandemic. Sentiment analysis emerged as an important approach to reveal changes in people's attitudes and emotions in facing this challenge. This research involves analyzing sentiment during the COVID-19 pandemic to understand the feelings, attitudes, and views of the community after the peak phase of the pandemic. This study refers to previous findings which show that the Random Forest Algorithm provides the highest accuracy in this analysis. Through testing with the Random Forest Algorithm method, model accuracy testing is carried out using a confusion matrix and comparing test data and training data in a ratio of 80:20. Test results show that this model achieves an accuracy rate of 91%, providing a more comprehensive view of changes in public sentiment during the COVID-19 pandemic.
Identification of Hoax News in the Using Community TF-RF and C5.0 Tree Decision Algorithm Enrico Budi Santoso; Yulison Herry Chrisnanto; Gunawan Abdillah
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 the rapid circulation of information through social media increases the risk that people receive and redistribute hoax news. Identifying hoax news is therefore important to support the circulation of reliable information, particularly political news. This research aims to create a system for identifying hoax news using TF-RF feature weighting and the C5.0 Decision Tree algorithm and to evaluate its classification performance. The study uses 1,000 news data obtained by web scraping with the keywords "election 2024", "politics", and "checkfaktapilkadamafindo" from Turnbackhoax.id and Detik.com. The processing stages include preprocessing, TF-RF word weighting, division of training and test data, C5.0 classification, and evaluation using a confusion matrix. Three training/test scenarios were evaluated. The 70/30 scenario produced 79.33% accuracy, 80.50% precision, and 97.01% recall; the 80/20 scenario produced 79.50% accuracy, 81.32% precision, and 95.48% recall; and the 90/10 scenario produced 72.00% accuracy, 74.39% precision, and 89.71% recall. Among the tested scenarios, the 80/20 split provided the highest accuracy. These findings show that the combination of TF-RF weighting and C5.0 can be implemented as an automatic classification approach for political hoax-news identification, while performance remains dependent on the composition of the training and testing data
Implementation of Random Forest Using Smote and Smoteenn in Customer Churn Classification in E-Commerce Muhammad Munzir Rizkya Mubarak; Yulison Herry Chrisnanto; Puspita Nurul Sabrina
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.v1i8.69

Abstract

Rapid internet growth has accelerated e-commerce expansion and intensified competition among platforms. Because customers can move to competing platforms that better meet their needs, customer churn has become an important problem requiring classification based on historical customer behavior. This study aimed to evaluate the performance of the Random Forest Classifier combined with SMOTE and SMOTEENN resampling techniques for handling imbalanced e-commerce customer churn data. The research involved data cleaning, selection, transformation, and resampling, followed by Random Forest parameter tuning using GridSearchCV. The dataset was divided into 70% training data and 30% testing data, and performance was evaluated using accuracy, precision, recall, F1-score, confusion matrix, and AUC. Random Forest with SMOTE produced the best overall balance, achieving 96.3% accuracy, 87.8% precision, 87.1% recall, 87.4% F1-score, and 93% AUC. Random Forest with SMOTEENN achieved the highest recall of 91.5% and an AUC of 92%; however, its precision decreased to 66.2%, indicating more false-positive predictions. These findings show that SMOTE provides a more balanced classification performance for the evaluated dataset, whereas SMOTEENN prioritizes churn detection at the cost of precision. Practically, selecting a resampling strategy should reflect whether balanced performance or maximum churn detection is the primary operational priority in practical customer-retention decisions.
Classification of Sentiment Towards BPJS Services Using the C50 Algorithm Amellia Fahezha Cahyaningrum; Yulison Herry Chrisnanto; Ade Kania Ningsih
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.v1i8.71

Abstract

Social media provides a timely source of public feedback on services delivered by the Social Security Administering Body for Health (BPJS Health), a State-Owned Enterprise responsible for Indonesia's public health insurance program. This study aimed to evaluate the ability of the C5.0 algorithm to classify positive and negative sentiment toward BPJS services in Twitter data. Applied quantitative research with an experimental text-classification design was conducted using a secondary dataset obtained from Kaggle. The implemented database displayed 3,060 documents. Data were processed through cleaning, case folding, tokenization, filtering, stemming, and TF-IDF weighting, followed by C5.0 classification and confusion-matrix evaluation using an 80:20 split. The reported test matrix comprised 621 cases: 6 true positives, 579 true negatives, 4 false positives, and 32 false negatives. These values produced 94.2% accuracy, 60.0% precision, and 15.8% recall. Although the aggregate accuracy was high, the low recall shows that the model detected only a small proportion of the positive class and was strongly influenced by the majority class. Therefore, the current model demonstrates the technical feasibility of applying C5.0 to BPJS-related tweets but cannot yet be considered balanced or fully reliable for service evaluation. Future optimization should address class imbalance, verify dataset labeling, and report complementary metrics before the results are used to support BPJS service-improvement decisions.
Co-Authors Adam, Marcellino Ade Kania Ningsih Ade Kania Ningsih Ade Kania Ningsih Ade Kania Ningsih, Ade Kania Aditya Prakasa Adryansyah Adryansyah Agung Wahana Ahmed Haikal Amellia Fahezha Cahyaningrum Andhika Karulyana Febrian Asep Id Hadianna Asep Saepul Ridwan Ashaury, Herdi Asri Maspupah Azzahra, Cynthia Nur Bania Amburika Benedictus Benny Sihotang Cecep M Zakariya Darmawan, Raja Dewi, Liony Puspita Didik Garbian Nugroho Drl, Indra Raja Eina, Muhammad Fikri eka rahmawati Emia Rosta Br. Sebayang Enrico Budi Santoso Erras Lindiarda Mahentar Fadilah, Rifal Fahmy Akhmad Firdaus Faiza Renaldi Fajar Tresnawiguna Fajri Rakhmat Umbara Farhan Naufal Febry Ramadhan Fitaloka, Intan Fuji Astari, Dhea Gerliandeva, Alfin Ghaniiy, Gheral Naza Gita Mahesa Gunawan Gunawan Abdilah Gunawan Abdillah Gunawan Abdillah Gunawan Abdillah Gunawan Abdillah Gunawan Abdillah, Gunawan Gunawan Abdullah Hadiana, Asep Id Hanafi, Willy Hanief Kuswanto, Muhammad Rafi Hendro Pudjiantoro, Tacbir Herdi Ashaury Herlina Napitupulu Herlinda Padillah Ibadirachman, Rifqi Karunia Id Hadiana , Asep Irawan, Joko Irma Santikarama Jeremia Oktavian Julian Evan Chrisnanto Julian Evan Chrisnanto Kamal, Angga Mochamad Kania Ningsih, Ade Kasyidi, Fatan Kharisma Jevi Shafira Sepyanto Kholidah Syaidah Komarudin, Agus Kukuh Yulion Setia Prakoso Luthfia Oktasari Mahendra, Lucky Syahroni Melina Melina Melina Muhamad Afnan, Zikri Muhammad Munzir Rizkya Mubarak Muhammad Rendy Raihan Mukti Kinani Mulianti, Adhani Musa Asyari Hidayat Jati Nabilla, Ulya Naufal, Farhan Nida Ulhasanah Norizan Mohamed Permana, Hary Permatasari, Nissa Aulia Prawira, Angga Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina, Puspita Nurul Puspo Dewi Dirgantari Putri Alifianti Wiyono, Tiara Putri Eka Prakasawati Raflialdy Raksanagara Rahandanu Rachmat Raja Darmawan Rayhan Irawan Razaki, Adam Rd Muhammad Alfajri Reza Noviandi Rezki Yuniarti Ridwan Ilyas RIDWAN INDRANSYAH Riyadi, Saiful Faris Rizal Dwiwahyu Pribadi Salsa Safira Nur Syamsi Sepyanto, Kharisma Jevi Shafira Siska Vadilah Sukono Sukono Sumantri, Fithra Aditya Taufiq Akbar Herawan Teguh Munawar Ahmad Tiara Rahmawati Valentina Adimurti Kusumaningtyas Wahyu Pratama, Raka Wawan Setiawan Widinastia, Audila Gumanty Widiyantoro, Widiyantoro Wildah Fatma Lestari Wina Witanti Wisnu Uriawan, Wisnu Yosia Oktavian Pailan Zizilia, Regitha