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All Journal Jurnal Algoritma Jurnal Teknologi Informasi dan Ilmu Komputer JAIS (Journal of Applied Intelligent System) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURTEKSI Jurnal Mantik Jurnal Teknik Informatika (JUTIF) Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT) Jurnal PkM MIFTEK Jurnal Algoritma INTERNAL (Information System Journal)
Indri Tri Julianto
Institut Teknologi Garut
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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THE PREDICTION OF PPA AND KIP-KULIAH SCHOLARSHIP RECIPIENTS USING NAIVE BAYES ALGORITHM Asri Mulyani; Dede Kurniadi; Muhammad Rikza Nashrulloh; Indri Tri Julianto; Meta Regita
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.4.297

Abstract

The aim of the research is was to predict the scholar recipient for Peningkatan Prestasi Akademik (PPA) and the Kartu Indonesia Pintar Kuliah (KIP-K). The prediction results of scholarship recipients will provide information in the form of the possibility of acceptance and non-acceptance of scholarship applicants. To achieve this goal, this study uses the Naive Bayes algorithm, where this algorithm predicts future opportunities based on past data by going through the stages of reading training data, then calculating the number of probabilities and classifying the values in the mean and probability table. The data analysis includes data collection, data processing, model implementation, and evaluation. The data needed for analysis needs to use data from the applicants for Academic Achievement Improvement (PPA) scholarship and the Indonesia Smart Education Card (KIP-K) scholarship. The data used for training data were 145 student data. The results of the study using the Naive Bayes algorithm have an accuracy of 80% for PPA scholarships and 91% for KIP-K scholarships.
Data Mining Algorithm Testing For SAND Metaverse Forecasting Indri Tri Julianto; Dede Kurniadi; Muhammad Rikza Nashrulloh; Asri Mulyani
Journal of Applied Intelligent System Vol 7, No 3 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i3.7155

Abstract

Metaverse is a technology that allows us to buy virtual land. In the future life in the real world can be duplicated into the Metaverse to increase efficiency, effectiveness, and a world without being limited by space and time. To buy land in the Metaverse, one can be done by using SAND. SAND is a crypto asset from a game called The Sandbox which functions as a transaction tool where in that game we can buy land and build it for various purposes just like we can store our Non-Fungible Tokens there. Metaverse is a digital business that will promise in the future because it offers easy and fast transactions. This study aims to compare the exact algorithm for making predictions about the SAND cryptocurrency used to buy Metaverse land. 7 algorithms are being compared, namely Deep Learning, Linear Regression, Neural Networks, Support Vector Machines, Generalized Linear Models, Gaussian Process, and K-Nearest Neighbors. The research method used is Knowledge Discovery in Databases. The research results show that the Support Vector Machines Algorithm has the most optimal Root Means Square Error value, root_mean_squared_error: 0.022 +/- 0.062 (micro average: 0.062 +/- 0.000). Based on this comparison, the Support Vector Machines Algorithm is suitable for predicting SAND Metaverse prices.
DATA MINING CLUSTERING FOOD EXPENDITURE IN INDONESIA Indri Tri Julianto; Dede Kurniadi; Muhammad Rikza Nashrulloh; Asri Mulyani
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.6.331

Abstract

The availability of food in a country is determined by a conducive climate. Prolonged droughts, floods, and natural disasters, especially for food crop production areas, will have an impact on the availability of natural disaster conditions faced by all countries including Indonesia is the Covid-19 pandemic, where this will affect food security in Indonesia. Data mining is the process of discovering the hidden meaning of a very large data set. The technique used in this study is Data Mining Clustering and the validity index used is Davies-Bouldin. This study aims to determine the Food Security Strategy in Indonesia through the Data Mining Clustering process based on food expenditure data and the Indonesian people's food expenditure per capita. The methodology used is Cross Industry Standard for Data Mining using the K-Means and K-Medoids Algorithm. The best cluster for the K-Means Algorithm is K=7 with a value of 0.341 and for the K-Medoids Algorithm, it is K=7 with a value of 0.362. This research produces the best algorithm, namely K-Means with a value of 0.341, which has a smaller value than K-Medoids with a value of 0.362. The results showed that the regional. cluster with the highest average expenditure on food was cluster 5 covering the DKI Jakarta area, while the cluster with the lowest expenditure was cluster 6 covering Central Java, East Nusa Tenggara, Southeast Sulawesi, Gorontalo, and West Sulawesi. In cluster 6, it is necessary to implement a strategy to increase food security by increasing production capacity and food reserves in each region.
Alternative Text Pre-Processing using Chat GPT Open AI Indri Tri Julianto; Dede Kurniadi; Yosep Septiana; Ade Sutedi
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 1 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i1.59746

Abstract

Text Pre-Processing is the first step in Sentiment Analysis. Categorizing a sentiment in a dataset is part of the Text-Preprocessing stage to get the optimal model accuracy value. Generative Pretrained Transformer, often known as Chat GPT, is a Machine Learning model that can automatically generate realistic and meaningful text. This study aims to examine the capability of GPT Chat as an alternative in the Text-Pre-Processing stage by utilizing GPT Chat 3 from the openai.com website in the Text-Pre-Processing stage of the collected tweet data. The data used in this research is the result of crawling Twitter by inserting the keyword "Chat GPT”. This study method was carried out by measuring performance using the K-Nearest Neighbor and Naïve Bayes Algorithms to find the best performance value and compare it with the Text-Preprocessing generated by Rapidminer. It is shown that the performance accuracy produced using the K-Nearest Neighbor Algorithm is 73.57% using the Linear Sampling method. The comparison result with the Text-Preprocessing method using Rapidminer indeed shows a better accuracy of 75.33%, which means it has a narrow difference of 1.76% with the Chat GPT Text Pre-Processing method. However, both are still in the same category, which is Fair Classification. The results of this research show that Chat GPT can be an alternative in Text-Preprocessing datasets for sentiment analysis.
Rancang Bangun Sistem Informasi Peminjaman dan Pengembalian Buku di Perpustakaan SMK Lugina Rancaekek Ricky Rohmanto; Indri Tri Julianto; Tria Afini
INTERNAL (Information System Journal) Vol. 6 No. 1 (2023)
Publisher : Masoem University

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

Abstract

The system of borrowing and returning books at the Library of SMK Lugina Rancaekek includes making loan reports, return reports, and member data is still done manually in the ledger, so errors often occur so that making reports takes a long time. The method used in this is the SLDC (System Development Life Cycle) method using Waterfall. Analysis and design of the system uses other tools such as Data Flow Diagram, Flowmap, Structure Chart, E-R Diagram, Data Dictionary, while the implementation uses Microsoft Visual Foxpro 9.0 and Microsoft Database Foxpro 9.0 for Database. The design of this system can facilitate the search for book data, speed up report generation, make it easier to control book stock, facilitate the process of borrowing and returning books so that they can produce information more quickly, precisely and accurately.
A Comparative Study of Alternative Automatic Labeling Using AI Assistant Julianto, Indri Tri; Kurniadi, Dede; Balilo Jr, Benedicto B.; Rohman, Fauza
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13950

Abstract

The development of AI assistants has become increasingly sophisticated, as evidenced by their growing adoption in assisting humans with various tasks. In particular, AI assistants have demonstrated potential in the field of sentiment analysis, where they can automate the labeling of text data. Traditionally, this labeling process has been performed manually by humans or using tools like the VADER Lexicon. This study is imperative to evaluate the performance of AI Assistants in sentiment labeling, as compared to traditional human-based labeling and the application of the VADER sentiment analysis algorithm. The methodology involves comparing the labeling results of Gemini and You AI with those of human labeling and VADER. Performance is evaluated using the Naive Bayes and K- Nearest Neighbour algorithms, and K-Fold Cross Validation is employed for evaluation. The results indicate that the performance of both AI assistants can closely approximate the performance of human labeling. Gemini's best accuracy is achieved with the k-NN algorithm at 53.71%, while You AI's best accuracy is achieved with the Naive Bayes algorithm at 48.30%. These results are close to the accuracy of human labeling (61.12%) using the k-NN algorithm and VADER (54.29%) using the Naive Bayes algorithm. This suggests that AI assistants can serve as an alternative for text data labeling, as the differences in performance are not statistically significant.
KULIAH KERJA NYATA UNTUK MENDUKUNG PENINGKATAN INDEKS PEMBANGUNAN MANUSIA DI DESA SUKARAME Julianto, Indri Tri; Citra Indahsari, Ajeng; Arif Syamsudin, Muhammad; Muhammad Ajif, Arvin; Akhdan Hidayat, Fairuz; Arif Rahman, Rifal; Suryani, Isma; Muhammad Sambas, Phadil; Malik Ibrahim, Maulana; Setiawan Putra, Achmad Dhani; Saepul Jamil, Alwis; Huwaidah, Alya; Mutiara, Sani; Agisni Nurlela, Agni; Dwi Anggara, Krisna; Rahman, Jaohari; Ridwan; Abdullah, Angga; Fauzi Pratama, Andhika; Alamsyah, Restu
Jurnal PkM MIFTEK Vol 4 No 2 (2023): Jurnal PkM MIFTEK
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/miftek/v.4-2.1471

Abstract

Real Work Lectures (KKN) is a form of implementation of the tridharma of higher education which integrates education, research and community service. The Garut Institute of Technology Thematic KKN in the 2022/2023 Academic Year is a clear example of this concept. In the Bayongbong District area, as many as 403 students from 5 study programs participated in this KKN program, with a focus on Strengthening Village Independence through the Use of Technology to Support Increasing the Human Development Index (HDI) in Sukarame Village. This KKN method is divided into three main stages, namely preparation, implementation and evaluation. During the implementation of KKN, students carry out various programs that cover various important aspects of society. They provide seminars on digital literacy, provide Healthy Home services, assist in tempeh waste management, develop the Kawal Desa Application, improve the quality of Micro, Small and Medium Enterprises (MSMEs) products, and provide education about nutrition and preventing anemia. Apart from these programs, the KKN group also actively participates in activities with the community, such as counseling about waste banks, teaching activities for children, teaching the Koran, environmental cleanup activities every Friday, joint sports activities, as well as various celebration activities in commemoration of Hari Birthday of the Republic of Indonesia. The results of implementing this KKN are clearly visible in improving the quality of life in Sukarame Village, Bayongbong, Garut Regency. A holistic approach that combines education, research and community service has brought significant positive benefits to local communities.
Analisis Sentimen Layanan Sistem Informasi Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Hidayat, Taupik; Cahyana, Rinda; Julianto, Indri Tri
Jurnal Algoritma Vol 21 No 1 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.21-1.1514

Abstract

AISnet For Students is an academic information system built by the Garut Institute of Technology to make it easier for students to carry out various campus academic administration activities online. This research aims to conduct sentiment analysis of online academic services at the Garut Institute of Technology by involving students as research subjects. This sentiment analysis will be carried out using the Naive Bayes Algorithm to explore student views and opinions regarding these academic services. This research was conducted with the aim of identifying potential problems that may occur in online academic services at the Garut Institute of Technology. Apart from that, this research also aims to provide recommendations that can help in improving the quality of these services. Research shows that students have positive sentiments towards academic services on campus. However, there are several problems that need to be overcome, such as technical problems and lack of features in the system. The solution to overcome this problem is to develop a user-friendly system, improve network quality, improve system features, conduct training or socialize the use of the system to students, and apply the latest technology and innovation in online student academic system services. The results of this research have the potential to provide benefits to educational institutions by helping to improve online academic services better. The results are expected to increase satisfaction and quality of services provided to students. Apart from that, this research can also be a reference or reference for further research related to sentiment analysis in the academic field or other fields. Where the Naive Bayes algorithm is used to analyze student sentiment towards academic services on the Garut Institute of Technology campus. The final results show that negative sentiment is greater than positive sentiment. Where negative sentiment is 54.75% and positive sentiment is 45.24%, this is because in the AISNet application most users provide reviews for the updates which are not real time. The following is the final result with an accuracy of 80.06%, a resolution of 83, 11 and recall 75.21.
Improvement of Data Mining Models using Forward Selection and Backward Elimination with Cryptocurrency Datasets Julianto, Indri Tri; Kurniadi, Dede; Fauziah, Fathia Alisha; Rohmanto, Ricky
Journal of Applied Intelligent System Vol. 8 No. 1 (2023): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i1.7568

Abstract

Cryptocurrency is a digital currency not managed by a state or central bank, and transactions are peer-to-peer. Cryptocurrency is still considered a speculative asset and its price volatility is relatively high, but it is also expected to become an efficient and secure transaction tool in the future. The purpose of this study is to compare and improve the performance of the Data Mining Algorithm model using the Feature Selection-Wrapper with the Binance Coin (BNB) cryptocurrency dataset. The Feature Selection-Wrapper approach used is Forward Selection and Backward Elimination. The algorithms used are Neural Networks, Deep Learning, Support Vector Machines, and Linear Regression. The methodology used is Knowledge Discovery in Databases. The results showed that from a comparison using K-Fold Cross Validation with a value of K=10, the Neural Network Algorithm has the best Root Mean Square Error value of 10,734 +/- 10,124 (micro average: 14,580 +/- 0,000). Then after improving performance using Forward Selection and Backward Elimination in the Neural Network Algorithm, the best performance improvement results are shown by using Backward Elimination with RMSE 5,302 +/- 2,647 (micro average: 5,805 +/- 0,000). 
Opinion Mining on Chat GPT based on Twitter Users Nashrulloh, Muhammad Rikza; Julianto, Indri Tri; Muzaky, Rifky Khoerul
Journal of Applied Intelligent System Vol. 8 No. 2 (2023): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i2.8399

Abstract

The presence of Chatbots can assist humans in their everyday lives. Chat GPT is one of the commonly used Chatbots that humans rely on to support their work, serve as an assistant, or even create artistic works or writings. The purpose of this research is to investigate opinions regarding the presence of Chat GPT. This Opinion Mining method is conducted by crawling data from Twitter, which can be categorized into three opinions: Positive, Negative, or Neutral. To calculate the accuracy level of the model created, two algorithms, Naïve Bayes and K-Nearest Neighbour, are compared. The model validation process utilizes K-Fold Cross Validation by varying the value of k (k=2, k=4, k=6, k=8, and k=10) and different sampling methods, namely Linear, Shuffled, and Stratified, to obtain optimal accuracy values. The research results indicate that the K-Nearest Neighbour Algorithm achieves the highest accuracy value of 92.40%. Based on this comparison, the K-Nearest Neighbour Algorithm is deemed suitable for modeling Opinion Mining of Chat GPT. The distribution of Twitter users' opinion percentages regarding Chat GPT is as follows: Positive 9.4%, Negative 1.4%, and Neutral 89%. Neutral opinions dominate the results of the conducted Opinion Mining.Keyword : chat GPT, opinion mining, twitter
Co-Authors Abdullah, Angga Abdulrohman, Muhammad Haviz Ade Sutedi Ade Sutedi, Ade Aditriyana, Muhammad Rizky Agisni Nurlela, Agni Akhdan Hidayat, Fairuz Alamsyah, Restu Ardana, Alwan Arif Rahman, Rifal Arif Syamsudin, Muhammad Asri Mulyani B. Balilo Jr , Benedicto B. Balilo Jr, Benedicto Balilo Jr, Benedicto B. Burhanudin, Asep Chaerunisa, Adinda Citra Indahsari, Ajeng Dede Kurniadi Dewi Tresnawati Dikdik, Dikdik Dinata, Messy Suryani Jaya Dwi Anggara, Krisna Dzulkhomzah, Moh Rival Fajar, Sigit Sihab Fauzi Pratama, Andhika Fauziah, Fathia Alisha Fikri Fahru Roji Fiqriansyah, Agung Firdaus, Ardy Reza Ginanjar, Ahmad Gotama, Dwi Hartono, Ali Hidayat, Ramdan Rahmat Hidayat, Rangga Huwaidah, Alya Ilham Maulana Ilyasin, Yasa Tiyas Kurnia, Ahmad Hopan Leni Fitriani, Leni Lindawati Lindawati Mahesa, Restu Gusti Malik Ibrahim, Maulana Meta Regita Muhammad Ajif, Arvin Muhammad Rikza Nashrulloh Muhammad Sambas, Phadil Mulyani, Neng Cici Munparik, Riyan Hakim Mutiara, Sani Muzaky, Rifky Khoerul N, Firza Much Asrizal Nawawi, Irfan Ahmad Nurandhini, Rosa Eliza Nurdiansyah, Farhan Nurdin, Kaila Fashla Nurfauziah, Hanifah Nurhalimah, Seli Nurhaqiqi, Lisda Nursalapiah, Sopa Nurul Muttaqin, Epwan Octaviansyah, Rizqi Moch Pardiansyah, Irgi Pratama, Rizky Muhammad Rahayu, Raden Erwin Gunadhi Rahman, Jaohari Rahmawati, Deby Ricky Rohmanto Ricky Rohmanto Ridwan Ridwan Setiawan Rinda Cahyana Rinda Cahyana Rohman, Fauza Rohmanto, Ricky Sadikin, M. Fitroh Saepul Jamil, Alwis Sanusi, Aini Fauziah Putu Septian Rheno Widianto Sermana, Elsa Maharani Setiawan Putra, Achmad Dhani Sirojudin, Naufal Suryadi, Khaila Thsabita Suryani, Isma Taupik Hidayat, Taupik Tria Afini Ujang Sarifudin Yoga Handoko Agustin Yosep Septiana