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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 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) 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
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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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 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%.
Perbandingan Improved K-Nearest Neighbour Dengan K-Nearest Neighbour Pada Analisis Sentimen Moda Raya Terpadu Jakarta Fahmy Akhmad Firdaus; Yulison Herry Chrisnanto; Puspita Nurul Sabrina
IJESPG (International Journal of Engineering, Economic, Social Politic and Government) Vol. 1 No. 3 (2023)
Publisher : IJESPG (International Journal of Engineering, Economic, Social Politic and Government)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

ABSTRAK K-Nearest Neighbour merupakan algoritma klasifikasi yang dikenal sebagai metode berbasis jarak. Improved K-Nearest Neighbour merupakan perkembangan dari K-Nearest Neighbour yang memiliki perbedaan pada bobot nilai k yang memiliki nilai tetap Permasalahan dalam penelitian ini adalah bagaimana akurasi metode iknn dibandingan pada analisis sentimen Moda Raya Terpadu Jakarta (MRT). MRT Jakarta merupakan sebuah transportasi umum yang menggunakan listrik di Jakarta yang diharapkan dapat mengurangi angka kemacetan di daerah Jakarta. Pengoprasian MRT yang sudah secara resmi banyak menimbulkan respon dari masyarakat, baik itu respon yang positif, negatif, maupun netral. Untuk mengetahui hal tersebut, analisis sentiment dapat digunakan untuk mengklasifikasikan sebuah kalimat. Hasil penelitian dengan eksperimen dataset yang tidak balance dan yang balance di setiap kelasnya, eksperimen nilai K dan beberapoa splitting data menunjukkan bahwa peningkatan akurasi metode Improved K-Nearest Neighbour terhadap K-Nearest Neighbour pada kasus analisis sentiment moda raya terpadu tidak signifikan, dengan akurasi 77.24%, precission sebesar 0.77, Recall sebesar 0.77, dan F1 Score sebesar 0.77. Sedangkan metode ­K-Nearest Neighbour memiliki akurasi sebesar 76.12%, dengan precission sebesar 0.76, Recall sebesar 0.76, dan F1 Score sebesar 0.76. Kata kunci: Improved K-Nearest Neighbour. K-Nearest Neighbour, MRT Jakarta ABSTRACT K-Nearest Neighbor is a classification algorithm known as the distance-based method. Improved K-Nearest Neighbor is a development of K-Nearest Neighbor which has a difference in the weight of the value of k which has a fixed value. The problem in this research is how the accuracy of the Improved K-Nearest Neighbor method is compared to the sentiment analysis of the Jakarta Integrated Raya Mode (MRT). MRT Jakarta is a public transportation that uses electricity in Jakarta which is expected to reduce congestion in the Jakarta area. The operation of the MRT which has officially elicited many responses from the public, be it positive, negative or neutral responses. To know this, sentiment analysis can be used to classify a sentence. The results of the research with unbalanced and balanced dataset experiments in each class, experiments on K values and some data splitting show that the increase in accuracy of the Improved K-Nearest Neighbor method against K-Nearest Neighbor in the case of integrated modal sentiment analysis is not significant, with an accuracy of 77.24 %, precision of 0.77, Recall of 0.77, and F1 Score of 0.77. While the K-Nearest Neighbor method has an accuracy of 76.12%, with a precision of 0.76, Recall of 0.76, and F1 Score of 0.76.
Klasifikasi Data Kesehatan Mental di Industri Teknologi Menggunakan Algoritma Random Forest Emia Rosta Br. Sebayang; Yulison Herry Chrisnanto; Melina Melina
IJESPG (International Journal of Engineering, Economic, Social Politic and Government) Vol. 1 No. 3 (2023)
Publisher : IJESPG (International Journal of Engineering, Economic, Social Politic and Government)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26638/ijespg.v1i3.57

Abstract

Abstract : Mental health is an integral part of human well-being. Mental health disorders can affect individuals in various aspects of life. Work pressure, heavy workload, and an unhealthy lifestyle can be the main causes of mental health disorders in the workplace, such as industrial technology. Employees' mental health problems in the workplace often do not receive enough attention because they cannot be seen physically. Mental health has a significant impact on the performance that will be shown by employees in contributing to the company, it requires the company's prudence and sensitivity in observing and understanding the mental health conditions of employees. In this study, the Open Source Mental Illness (OSMI) survey data was classified using the Random Forest algorithm with the ensemble method, as well as the bootstrap tree method to improve the performance of the Random Forest algorithm in determining the accuracy of mental health data. The Random Forest algorithm is an ensemble learning method that combines several decision trees to improve prediction accuracy. Classification is carried out using a bootstrap tree which takes training data to train a model or ensemble so that it can take patterns and relations from the data to carry out classification, the Random Forest algorithm is an ensemble learning method that combines several decision trees for research with 80% training data and 20 test data %. The results of this study indicate a fairly good level of accuracy, which is 84%, so that it can make an important contribution in understanding the level of mental health disorders experienced by technology industry employees. The expected results of this research can improve the quality of life and productivity of employees at work.
OPTIMASI PARAMETER ARTIFICIAL NEURAL NETWORK MENGGUNAKAN ALGORITMA GENETIKA PADA PERAMALAN TOKEN METAVERSE SANDBOX Zikri Muhamad Afnan; Yulison Herry Chrisnanto; Tacbir Hendro Pudjiantoro
Jurnal Locus Penelitian dan Pengabdian Vol. 2 No. 10 (2023): jurnal locus penelitian dan pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v2i10.1733

Abstract

Sandbox (SAND) adalah salah satu token utama dalam ekosistem Metaverse the Sandbox, Harga token sandbox (SAND) sangat dipengaruhi oleh faktor-faktor seperti permintaan, ketersediaan, dan berita terkait proyek, dengan adanya volatilitas yang tinggi dalam harga SAND. Tujuan penelitian ini adalah untuk menganalisis kinerja algoritma genetika dalam mengoptimasi ANN dalam memprediksi harga close (Penutup) token metaverse sandbox. Sistem prediksi harga token metaverse sandbox menggunakan metode optimasi artificial neural network dengan algoritma genetika, untuk melihat fluktuasi harga token sandbox terhadap rupiah di masa yang akan datang. Penelitian yang dilakukan pada data time series nilai tukar Rupiah terhadap token Sandbox periode 14 Agustus 2020 hingga 30 Juni 2023 menggunakan optimasi Artificial Neural Network (ANN) dengan algoritma genetika. Hasil penelitian mengungkapkan bahwa algoritma genetika mampu digunakan untuk menentukan kombinasi parameter terbaik pada ANN dalam melakukan prediksi nilai token Sandbox. Selain itu, pengujian akurasi juga menunjukkan penurunan tingkat error dari RMSE Train 1747 RMSE Test 424 menjadi RMSE Train 1028 RMSE Test 324, mengindikasikan terjadinya peningkatan akurasi dalam sistem prediksi. Penelitian ini dilakukan pada data time series nilai tukar Rupiah terhadap token Sandbox yang mencakup 1052 record dari periode 14 Agustus 2020 hingga 30 Juni 2023 dengan menggunakan optimasi Artificial Neural Network (ANN) melalui algoritma Genetika.
STUDENT GRADUATION PREDICTION SYSTEM BASED ON ACADEMIC AND NON-ACADEMIC (EQ) DATA USING C4.5 ALGORITHM Willy Hanafi; Yulison Herry Chrisnanto; Ade Kania Ningsih
JUMANJI (Jurnal Masyarakat Informatika Unjani) Vol 7 No 1 (2023): Jurnal Masyarakat Informatika Unjani
Publisher : Jurusan Informatika Universitas Jenderal Achmad Yani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26874/jumanji.v7i1.272

Abstract

The graduation profile is an important element for higher education accreditation standards. It reflects the performance of the adopted education system within a certain period. The better the profile graduation, the better the value of the accreditation. Some students are unable to complete their studies on time or even fail to complete their studies because they exceed the specified time limit, which is seven years, and it negatively affects institutions' accreditation. To prevent this from happening, it is necessary to know what obstacles that cause these students could not complete their studies on time. by knowing this information, prevention can be done for students who are potentially unable to complete their studies on time. The purpose of this study was to make a system that can predict the graduation timeline and the factors that influence it. The data used was graduation data from undergraduate students majoring in psychology from 2015 to 2017 at a university in Cimahi. The data had a total record of 461 students, 44 subject value attributes, 13 psychotest attributes, and class attributes. We generated the result by using decission tree method with C4.5 algorithm, which produces 90.32% accuracy. The depth of the tree can also influence the accuracy of the algorithm. This study also found that academic and non-academic (EQ) scores can affect students’ graduation time.
Sistem Klasifikasi Pola Perilaku Bendungan Menggunakan K-Nearest Neighbor Berbasis Data Temporal Di Bendungan Djuanda Fajar Tresnawiguna; Yulison Herry Chrisnanto; Puspita Nurul Sabrina
In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) Vol 19 No 2 (2020): In Search
Publisher : LPPM UNIBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37278/insearch.v19i2.348

Abstract

Dam had a vital role in the social and economic communities, including water providers for irrigation, industry, power plant, recreation, and flood control. Besides having various benefits, Dam also holds the potential of disaster. Preventive action in disaster management was necessary. Dam behavior pattern classification with computational assistance was needed to determine the results of determining accurate, fast, and efficient behavior patterns. In this research, the system made by using a K-NN Classifier based on temporal data that formed to predict the behavior patterns of dams in the Djuanda Dam. The system test results produce the best accuracy of 88% for safe labels, 82% for standby labels and 92% for alert labels with the best k value is 8.
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

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 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

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.
Co-Authors Adam, Marcellino Ade Kania Ningsih Ade Kania Ningsih Ade Kania Ningsih Aditya Prakasa Adryansyah Adryansyah Agung Wahana Agus Komarudin 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 Didik Garbian Nugroho Drl, Indra Raja Eina, Muhammad Fikri Eka Rahmawati Emia Rosta Br. Sebayang Enrico Budi Santoso 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 Abdillah, Gunawan Gunawan Abdullah Gunawan Gunawan Hadiana, Asep Id 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 Melina Melina Melina Melina Melina, Melina Muhammad Munzir Rizkya Mubarak Muhammad Rendy Raihan Mukti Kinani Mulianti, Adhani Musa Asyari Hidayat Jati Nabilla, Ulya 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 Razaki, Adam Rd Muhammad Alfajri Reza Noviandi Rezki Yuniarti Ridwan Ilyas RIDWAN INDRANSYAH Riyadi, Saiful Faris Rizal Dwiwahyu Pribadi Salsa Safira Nur Syamsi Santikarama, Irma Sepyanto, Kharisma Jevi Shafira Siska Vadilah Sukono . Sumantri, Fithra Aditya Tacbir Hendro Pudjiantoro 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 Willy Hanafi Wina Witanti Wisnu Uriawan, Wisnu Yosia Oktavian Pailan Zikri Muhamad Afnan Zizilia, Regitha