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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 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) 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 J-Icon : Jurnal Komputer dan Informatika IJESPG (International Journal of Engineering, Economic, Social Politic and Government) journal Enrichment: Journal of Multidisciplinary Research and Development 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
Program Studi Informatika, Fakultas Sains Dan Informatika Universitas Jenderal Achmad Yani
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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Identifikasi Berita Palsu di Portal Media Online Menggunakan Model IndoBERT dan LSTM Kamal, Angga Mochamad; Chrisnanto, Yulison Herry; Yuniarti, Rezki
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8660

Abstract

The rapid spread of political fake news on Indonesian online media portals poses serious threats to public trust and democratic stability. The main research problem is the limitation of existing models in handling the complexity of Indonesian political narratives containing local idioms and long text structures. The proposed solution employs a hybrid IndoBERT-LSTM model with ensemble stacking approach using logistic regression meta-learner to optimize fake news detection. IndoBERT is selected to capture Indonesian language nuances, while LSTM handles sequential dependencies in long articles. The research objective is to develop an accurate detection system for political fake news by leveraging the complementary strengths of both models. The dataset comprises 32,218 political articles from credible portals (Kompas, CNN Indonesia, Tempo, Detiknews, Viva) and Turnbackhoax.id validation from September 2021 to December 2024. Research results demonstrate that ensemble stacking achieves superior performance with F1-score 0.9544, accuracy 95.41%, and AUC-ROC 0.9936, outperforming standalone IndoBERT (F1: 0.9542) and LSTM (F1: 0.9417). Error analysis identifies 4.59% error rate with 134 false positives and 88 false negatives, particularly in long articles (average 2,739 characters). This model has potential for integration into fact-checking platforms for real-time detection of Indonesian political fake news.
Sistem Rekomendasi Penawaran Produk Pada Online Shop Menggunakan K-Means Clustering Naufal, Farhan; Herry Chrisnanto, Yulison; Kania Ningsih, Ade
Informatics and Digital Expert (INDEX) Vol. 4 No. 1 (2022): INDEX, Mei 2022
Publisher : LPPM Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/index.v4i1.879

Abstract

Online Shop adalah salah satu fasilitas yang disajikan oleh internet, yang mampu mempermudah masyarakat dalam belanja tanpa harus bertatap muka dengan pelanggan, tanpa harus antri dan tawar menawar. Pertumbuhan ekonomi digital semakin besar persaingan bisnis juga akan semakin berat, akibatnya semakin banyak online shop tidak hanya menampilkan produk-produk tetapi juga perlu didukung oleh pemilihan produk yang tepat untuk menarik perhatian pelanggan. Terlalu banyaknya variasi produk yang ditawarkan secara random (acak) pada online shop membuat beberapa pelanggan kesulitan dalam menentukan produk yang akan dibeli. Berdasarkan permasalahan yang muncul maka penelitian mengenai Sistem Rekomendasi Penawaran Produk Pada Online Shop Menggunakan K-Means Clustering ini dilakukan. Sistem ini menggunakan algoritma K-Means Clustering serta dataset yang digunakan adalah data transaksi penjualan dari kurun waktu 1 tahun terakhir agar cakupanya tidak meluas dengan menggunakan data terbaru. Hasil dari penelitian ini ditemumakan bahwa ada 3 cluster yang memiliki karakteristik berbeda yaitu, cluster 1 dengan karakteristik penjualan sedang dengan rentang umur pembeli 36-50 tahun , cluster 2 dengan karakteristik penjualan terbanyak dengan rentang umur pembeli 18-26 tahun dan cluster 3 dengan karakteristik penjualan rendah dengan rentang umur 27-35 tahun. Dari hasil cluster dapat disimpulkan bahwa produk yang direkomendasikan merupakan produk terpopuluer dari setiap clusternya. Hasil perhitungan nilai sillhouette coeficient didapatkan cluster dengan jumlah 3 karena memiki nilai paling mendekati Si = 1 yaitu dengan nilai 0.7354092263523232.
ANALISIS CLUSTER PADA KELOMPOK MASYARAKAT YANG RENTAN TERHADAP PAPARAN COVID-19 MENGGUNAKAN METODE K-MEANS CLUSTERING DAN VISUALIASI DENGAN SIG Drl, Indra Raja; Chrisnanto, Yulison Herry; Umbara, Fajri Rakhmat
Informatics and Digital Expert (INDEX) Vol. 4 No. 2 (2022): INDEX, November 2022
Publisher : LPPM Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/index.v4i2.885

Abstract

Covid-19 adalah penyakit yang menular serta laju infeksi yang cepat,setelah mencapai 100 kasus yang dikonfirmasikan terinfeksi tingkat penyebarannya meluas, Dengan cepatnya penyebaran wabah Covid-19 masyarakat sangat prihatin dengan penyebaran dan dampaknya ,orang yang sebelumnya sudah memiliki gangguan kesehatan akan meningkatkan risiko terinfeksi Covid-19 gangguan kesehatan ini seperti,tuberkulosis,diabetes ,diare ,hipertensi.Ada pun Faktor lain yang mempengaruhi penyebaran Covid-19 sepert kepadatan penduduk yang tinggi di kota besar ,iklim,suhu dan daerah metropolitan merupakan faktor risiko utama untuk tertular virus. Dari adanya faktor yang mempengaruhi kasus covid-19 sehingga Satgas Penanganan Covid-19 menilai pentingnya bagi semua pihak termasuk masyarakat memahami faktor-faktor lonjakan kasus Covid-19 agar terhindar dari kasus itu.tujuan dari penelitian ini Menggunakan metode K-Means Clustering untuk analisis cluster pada wilayah yang memiliki karakteristik tingginya kasus covid-19 dan variable apa yang berpengaruh terhadap tingginya kasus covid-19 dan divisualisasi menggunakan Sistem informasi geografis sehingga diharapakan dapat menjadi informasi bagi masyarakat dan instansi kesehatan untuk memahami kelompok wilayah yang rentan. kesimpulannya wilayah kota bandung dikelompokan menjadi 3 cluster yang dimana cluster 1 itu wilayah dengan kasus covid-19 tertinggi dan faktor yang mempengaruhi covid-19 juga tinggi untuk cluster 2 memiliki tingkat kasus yang rendah dan cluster 3 memiliki tingkatan yang yang lebih rendah dari kedua cluster.
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%.
Implementation of Random Forest Using Smote and Smoteenn in Customer Churn Classification in E-Commerce Mubarak, Muhammad Munzir Rizkya; Chrisnanto, Yulison Herry; Sabrina, Puspita Nurul
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

The rapid development of the internet is one of the driving factors behind the growth of e-commerce. This has led to the emergence of many e-commerce companies, resulting in intense competition among them. Customers have the right to choose the e-commerce platforms that suit their needs and can switch to competing e-commerce platforms, a phenomenon known as customer churn. This issue can be addressed by classifying customer behavior based on existing data. This study utilizes the Random Forest Classifier method, employing the SMOTE and SMOTEENN resampling techniques to handle data imbalance. From the conducted research, the best results were achieved using the SMOTE implementation, with an accuracy of 96.3%, precision of 87.8%, recall of 87.1%, f1-score of 87.4%, and an AUC score of 93%. These results successfully strike a balance between recognizing the positive class (churn) and controlling false positives. On the other hand, the SMOTEENN implementation yields the best recall value and an increase in AUC score, but it comes with a significant decrease in precision, indicating a challenge in controlling false positives.
Classification of Sentiment Towards BPJS Services Using the C50 Algorithm Cahyaningrum, Amellia Fahezha; Chrisnanto, Yulison Herry; Ningsih, Ade Kania
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

This public health insurance program for all Indonesian people is supervised by the Social Security Administering Body (BPJS) for Health, an Air-Owned Enterprise. Thus, it will be easier for the public to find information about what policies the government has implemented to regulate BPJS. One of them is that people can find information on the social network Twitter. Due to its ease and simplicity of use, the number of tweets can easily grow quickly, which is why Twitter is more popular among Indonesians for communicating. Twitter is widely used as a promotional medium as well as a means of expressing opinions regarding criticism, suggestions, issues, and opinions of a public nature such as the views of netizens on new government policies and so on. One of them is in BPJS services, the large number of BPJS users causes BPJS to provide feedback services to users to find out how many good and bad responses to BPJS services. Sentiment classification is a branch of text mining. Sentiment classification is very basic in the evaluation process of a topic problem. Then the sentiment classification has the main objective of finding out the polarity of positive, and negative sentiment. The c50 algorithm method is one of the methods that can be used in the classification of BPJS service sentiment. In this research, the classification of BPJS service sentiment through Twitter media was carried out using the C50 algorithm method.
Talk show segmentation system based on Twitter using K-medoids clustering algorithm Sepyanto, Kharisma Jevi Shafira; Chrisnanto, Yulison Herry; Umbara, Fajri Rakhmat
Jurnal Pendidikan Teknologi Kejuruan Vol 3 No 3 (2020): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jptk.v3i3.15123

Abstract

Innovations on a talk show on television can be a threat. Audience will be divided into groups so that it can make a downgrade rating program. Program ratings affect companies that will use advertising services. Television companies will go bankrupt. The biggest source of income is sales of advertising services. One way to overcome them can be analyzed in public opinion. The results of the analysis can provide information about the attractiveness of the community towards the program. But the analysis process takes a long time and can be done only by a competent person so another process is needed to get the results of the analysis that is fast and can be done by anyone. In this study using K-Medoids Clustering in the process of identifying public opinion. The clustering process known as unsupervised learning will be combined with the labeling process. The previous episode's tweet data will be labeled and then used to obtain the predicted labels from other cluster members. Before going through the clustering stage, the tweet data will go through the text preprocessing stage then transformed into a numeric form based on the appearance of the word. Transformation data will be clustered by calculating proximity using Cosine Similarity. Labels from the Medoids cluster will be used on unlabeled tweet data. The cluster results were tested using the Silhouette Coefficient method to get 0.19 results. However, this method successfully predicted public opinion and achieved an accuracy of 80%.
Analisis Sentimen Terhadap Ulasan Aplikasi Deepseek AI Menggunakan Model Bidirectional LSTM dan IndoBert Mahendra, Lucky Syahroni; Herry Chrisnanto, Yulison; Yuniarti, Rezki
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.22-2.2950

Abstract

Advancements in Natural Language Processing (NLP) technology have progressed rapidly, marked by the emergence of various Large Language Models (LLMs) such as ChatGPT, Gemini, and DeepSeek AI. One particularly popular model is DeepSeek AI due to its ability to understand and respond to natural language text more contextually. The increasing popularity of this application is accompanied by a growing number of user reviews, which serve as an important source of data for capturing their experiences and perceptions. This study aims to analyze user sentiment toward the DeepSeek AI application using a deep learning approach. Specifically, the research focuses on evaluating the performance of sentiment classification models in the context of Indonesian-language data, which is relatively limited and imbalanced. The dataset was collected from user reviews on the Google Play Store and categorized into three sentiment classes: positive, negative, and neutral. The method employed is a combination of IndoBERT and Bidirectional Long Short-Term Memory (BiLSTM). IndoBERT is used to generate contextual text representations in Indonesian, while BiLSTM is utilized to recognize sequential word patterns. Experimental results show that this hybrid model achieves an accuracy of 45%, with the highest F1-score of 0.66 in the positive class. Meanwhile, a macro-average F1-score of 0.33 and a ROC-AUC of 0.546 indicate that the model’s performance remains limited in distinguishing the three classes evenly. Nevertheless, the main contribution of this study lies in the development of a new dataset consisting of 1,774 Indonesian-language reviews related to LLM-based applications, which can be used for further research in the field of Natural Language Processing (NLP). The study also demonstrates the effectiveness of integrating IndoBERT and BiLSTM for sentiment analysis of Indonesian text with imbalanced data distribution.
Co-Authors Adam, Marcellino Ade Kania Ningsih Ade Kania Ningsih Ade Kania Ningsih, Ade Kania Aditya Prakasa Adryansyah Adryansyah Agung Wahana Agus Komarudin Andhika Karulyana Febrian Asep Id Hadianna Asep Saepul Ridwan Ashaury, Herdi Asri Maspupah Azzahra, Cynthia Nur Bania Amburika Benedictus Benny Sihotang Cahyaningrum, Amellia Fahezha Cecep M Zakariya Darmawan, Raja Dewi, Liony Puspita Didik Garbian Nugroho Drl, Indra Raja Eina, Muhammad Fikri Eka Rahmawati Emia Rosta Br. Sebayang Fadilah, Rifal Fahmy Akhmad Firdaus Faiza Renaldi, Faiza Fajar Tresnawiguna Fajri Rakhmat Umbara Fajri Rakhmat Umbara Farhan Naufal Febry Ramadhan Fitaloka, Intan Fuji Astari, Dhea Gerliandeva, Alfin Gita Mahesa Gunawan Abdillah Gunawan Abdillah Gunawan Abdillah Gunawan Abdillah, Gunawan Gunawan Abdullah Gunawan Gunawan Hadiana, Asep Id Haikal, Ahmed Hanafi, Willy Hanief Kuswanto, Muhammad Rafi Hendro Pudjiantoro, Tacbir Herdi Ashaury Herlina Napitupulu Herlinda Padillah Ibadirachman, Rifqi Karunia Id Hadiana , Asep Irma Santikarama Joko Irawan Julian Evan Chrisnanto Kamal, Angga Mochamad Kania Ningsih, Ade Kasyidi, Fatan Kharisma Jevi Shafira Sepyanto Kholidah Syaidah Kukuh Yulion Setia Prakoso Kusumaningtyas, Valentina Adimurti Luthfia Oktasari Mahendra, Lucky Syahroni Melina Melina Melina Melina Melina, Melina Mubarak, Muhammad Munzir Rizkya Muhamad Afnan, Zikri 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 Puspo Dewi Dirgantari Putri Alifianti Wiyono, Tiara Putri Eka Prakasawati Raflialdy Raksanagara Rahandanu Rachmat Raja Darmawan Razaki, Adam Rd Muhammad Alfajri Reza Noviandi Rezki Yuniarti Ridwan Ilyas RIDWAN INDRANSYAH Riyadi, Saiful Faris Rizal Dwiwahyu Pribadi Santikarama, Irma Santoso, Enrico Budi Sepyanto, Kharisma Jevi Shafira Siska Vadilah Sukono . Sumantri, Fithra Aditya Taufiq Akbar Herawan Teguh Munawar Ahmad Tiara Rahmawati Umbara, Fajri Rakhmat 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