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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 RESTI (Rekayasa Sistem dan Teknologi Informasi) 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) Journal of Intelligent Systems Technology and Informatics International Journal of Applied Information Systems and Informatics
Indri Tri Julianto
Department Of Computer Science, Institut Teknologi Garut, Indonesia
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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Time Series Forecasting of Top 3 Ranking Cryptocurrencies Setiawan, Ridwan; Julianto, Indri Tri; Roji, Fikri Fahru
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.v8i2.8435

Abstract

Cryptocurrency has become a phenomenon worldwide. Although not all countries have legalized it, it is considered a promising investment asset. Currently, there are three top-ranking cryptocurrencies: Bitcoin, Ethereum, and Tether. This research aims to compare the performance of five forecasting algorithms, namely Autoregressive Integrated Moving Average (ARIMA), Neural Network, Support Vector Machine, Linear Regression, and Generalized Linear Model, using the dataset of Bitcoin, Ethereum, and Tether cryptocurrencies. The research methodology employed is Knowledge Discovery In Databases (KDD). The technique involves assessing the performance based on the Root Mean Square Error (RMSE) and comparing the results to find the most optimal model performance. The research findings indicate that for Bitcoin cryptocurrency, the Neural Network algorithm produced the most optimal results with an RMSE of 9180.534. For Ethereum cryptocurrency, the Neural Network algorithm demonstrated the best performance with an RMSE value of 537.528. Furthermore, for Tether cryptocurrency, the ARIMA algorithm yielded the best performance with an RMSE value of 0.003. Keywords – bitcoin, cryptocurrency, ethereum, forecasting, tether
Segmentasi Wilayah Terdampak Bencana Berdasarkan Fitur Geo-Posisi Sutedi, Ade; Julianto, Indri Tri; Fitriani, Leni
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 4: Agustus 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.1148557

Abstract

Penelitian ini memperkenalkan prototipe aplikasi segmentasi wilayah terdampak bencana (DAS-Apps) untuk melakukan segmentasi wilayah terdampak bencana berdasarkan fitur latitude dan longitude (geo-posisi). Aplikasi ini berfungsi untuk menyeleksi informasi bencana dari media sosial, data resmi pemerintah dari Badan Nasional Penanggulangan Bencana (BNPB), dan informasi bencana yang dikirimkan melalui DAS-Apps secara real-time. Daerah terdampak dipetakan berdasarkan data geo-posisi kemudian dihitung menggunakan metode Haversine Formula untuk menunjukkan peristiwa bencana terjadi dan seberapa jauh jangkauan bencana dirasakan. Pada penelitian ini, simulasi DAS-Apps dilakukan menggunakan dataset gempa (M ≥ 5.0) yang berasal dari Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) pada rentang bulan November dan Desember 2022 khusunya data bencana gempa bumi untuk wilayah Cianjur, Indonesia. Hasil pengujian menunjukkan bahwa prototipe DAS-Apps dapat melakukan proses segmentasi wilayah berdasarkan radius geo-posisi dari titik informasi bencana sehingga dapat diimplementasikan untuk untuk framework aplikasi tanggap darurat dan manajemen bencana pada penelitian selanjutnya.   Abstract   This research introduces a prototype Disaster-affected Area Segmentation Application (DAS-Apps) designed to perform segmentation of disaster-affected areas based on latitude and longitude features (geo-positioning). The application functions to filter disaster information from social media, official government data from Badan Nasional Penanggulangan Bencana (BNPB), and disaster information submitted in real-time through DAS-Apps. The affected areas are mapped based on geo-positioning data, and then calculated using the Haversine Formula method to indicate when and how far-reaching the disaster events are perceived. In this study, DAS-Apps simulations were conducted using earthquake datasets (magnitude ≥ 5.0) from the Meteorology, Climatology, and Geophysics Agency (BMKG) during the months of November and December 2022, specifically earthquake data for the Cianjur region, Indonesia. The test results indicate that the DAS-Apps prototype can successfully carry out the area segmentation process based on the geo-positioning radius from the disaster information point, making it suitable for implementation in emergency response and disaster management application frameworks in future research.
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.
THE ROLE OF FEATURE SELECTION IN ENHANCING THE ACCURACY OF AI ASSISTANT AUTO-LABELING Julianto, Indri Tri; Kurniadi, Dede; B. Balilo Jr, Benedicto; Rohman, Fauza
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 11, No 1 (2024): Desember 2024
Publisher : Universitas Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i1.3364

Abstract

Abstract: The development of AI assistants such as Gemini and ChatGPT can significantly assist in daily human tasks. In the field of Sentiment Analysis, AI assistants can be utilized as an automated labeling alternative to provide positive, negative, or neutral sentiments within a dataset. This research aims to enhance the performance of AI assistants in automated labeling processes by employing the Feature Selection algorithm, specifically Forward Selection. The methodology involves utilizing the Naïve Bayes and K-NN algorithms, and subsequently improving accuracy through the Feature Selection algorithm. The evaluation is conducted using K-Fold Cross Validation. Research findings indicate an improvement in the accuracy of the best model, which is ChatGPT, when using the Naïve Bayes algorithm and Shuffled Sampling technique. The initial accuracy of 79.09% increased to 87.18% after Feature Selection was applied. This demonstrates the effectiveness of Feature Selection, particularly Forward Selection, in enhancing the accuracy performance of the model.            Keywords: ai; assistant; chat gpt; feature selection; gemini.  Abstrak: Pekembangan Asisten AI seperti Gemini dan Chat GPT dapat membantu pekerjaan manusia sehari-hari. Dalam bidang Analisis Sentimen, Asisten AI dapat digunakan sebagai alternatif pelabelan otomatis untuk memberikan sentimen positif, negatif atau netral dalam suatu dataset. Penlitian ini bertujuan untuk meningkatkan performa yang dihasilkan oleh Asisten AI dalam proses pelabelan otomatis menggunakan Algortima Feature Selection yaitu Forward Selection. Metode yang digunakan adalah dengan menggunakan Algoritma Naïve Bayes dan K-NN kemudian hasil akurasi akan ditingkatkan menggunkan Algoritma Feature Selection. Evaluasi yang digunakan adalah K-Fold Cross Validation. Hasil penelitian menunjukkan peningkatan akurasi model terbaik berada pada Chat GPT dengan menggunakan Algoritma Naïve Bayes dan Teknik Shuffled Sampling, dari nilai akurasi awal sebesar 79.09%, setelah ditingkatkan menggunakan Feature Selection, maka nilai akurasinya meningkat menjadi 87.18%. Hal ini membuktikan peran Feature Selection, dimana yang digunakan adalah Forward Selection dalam meningkatkan akurasi ternyata memang efektif dalam meningkatkan performa akurasi model. Kata kunci: ai; assisten; chat gpt; feature selection; gemini 
PEMBERDAYAAN UMKM, PELESTARIAN BUDAYA LOKAL, DAN PENGELOLAAN SAMPAH DI DESA NGAMPLANG Julianto, Indri Tri; Ginanjar, Ahmad; Mahesa, Restu Gusti; Munparik, Riyan Hakim; Kurnia, Ahmad Hopan; Sirojudin, Naufal; N, Firza Much Asrizal; Suryadi, Khaila Thsabita; Ilyasin, Yasa Tiyas; Nurdin, Kaila Fashla; Rahmawati, Deby; Fiqriansyah, Agung; Nurandhini, Rosa Eliza; Hartono, Ali; Gotama, Dwi; Nurhalimah, Seli; Maulana, Ilham; Burhanudin, Asep; Firdaus, Ardy Reza
Jurnal PkM MIFTEK Vol 5 No 2 (2024): Jurnal PkM MIFTEK
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/miftek/v.5-2.1890

Abstract

Pelaksanaan KKN Tematik ITG 2024 di Desa Ngamplang menghadapi permasalahan kurangnya pengelolaan sampah di fasilitas umum, minimnya plang penunjuk arah jalan, rendahnya pemanfaatan teknologi digital oleh pelaku UMKM, serta kurangnya eksposur budaya dan kesenian lokal. Program kerja KKN Kelompok 8 bertujuan untuk meningkatkan kesadaran masyarakat terhadap pentingnya pengelolaan sampah, membantu pengguna jalan melalui pemasangan plang penunjuk arah, mendukung pelaku UMKM dalam pemanfaatan teknologi digital untuk pemasaran, serta memperkenalkan budaya dan kesenian lokal. Tahapan pelaksanaan dimulai dari survei lokasi dan wawancara, perencanaan kegiatan, penyediaan alat dan bahan, hingga pelaksanaan program kerja secara door to door. Hasilnya, terdapat 5 buah tong sampah yang di sebarkan ke tempat dan fasilitas umum seperti Lapangan Sepak Bola, Sekolah dan Mesjid, terdapat 3 plang arah di Desa Ngamplang, 7 UMKM yang telah dibantu menngunakan platform digital, dan 8 kesenian lokal yang di tampilkan pada acara pagelaran kesenian. Dampak kegiatan ini terlihat pada peningkatan kesadaran lingkungan, akses informasi yang lebih baik, serta kemajuan UMKM lokal. Untuk keberlanjutan, diharapkan adanya pelatihan rutin bagi masyarakat dalam pengelolaan sampah dan penggunaan teknologi digital, serta pengembangan program seni budaya lokal agar tetap dikenal luas.
Sentiment Analysis Using Grok AI as an Auto-Labeling Tool in The Text Processing Stage Agustin, Yoga Handoko; Kurniadi, Dede; Julianto, Indri Tri; B. Balilo Jr , Benedicto
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14632

Abstract

A critical aspect of Natural Language Processing (NLP) is text processing, where text labeling represents the most significant challenge due to its resource-intensive nature when conducted manually. At this stage, automatic labeling emerges as a more practical solution, particularly with the advent of Artificial Intelligence (AI), which offers tools to address this obstacle. Grok AI, equipped with a new feature operable on Platform X, provides a promising approach. This study aims to leverage the Grok AI feature on Platform X for automatic text labeling. The research methodology involves labeling text data obtained from a public dataset. To assess the quality of the labeling results, an evaluation method employing Naive Bayes classification modeling is applied. The findings reveal that Grok AI's performance closely approximates that of human labeling. The highest accuracy achieved by Grok AI is 51.71% using the k-Nearest Neighbors (k-NN) algorithm, approaching the human labeling accuracy of 60.52% with k-NN. Furthermore, Grok AI surpasses the performance of VADER labeling, which achieves an accuracy of only 49.49% with Naive Bayes. Consequently, the Grok AI feature on Platform X presents a viable alternative for the automatic labeling of text data.
Rancang Bangun Virtual Reality Tour Sebagai Inovasi Media Promosi Wisata Ecopark Berbasis Website Chaerunisa, Adinda; Julianto, Indri Tri
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.1687

Abstract

Garut is home to a number of pine forest ecoparks and natural attractions. These pine forests in Garut are known for their amazing natural attractions. Conventional media such as images, videos and texts have been used by natural attractions in Garut City as promotional media for disseminating information to tourists. However, this conventional media is less helpful because it only displays still images, videos and texts which are less interactive and real. Utilization of current technological advances such as Virtual Reality, can be applied to the media promotion of Ecopark natural attractions, especially pine forests as improving the experience of visitors before visiting tourist attractions so that visitors can consider appropriate transportation, distance, and road conditions to that location. The purpose of this research is to design a Virtual Reality Tour of Ecopark Nature Tourism that can be used as a suggestion of places to visit and provide information to prospective visitors. The method used in this research is the Multimedia Development Life Cycle with six stages, namely concept, design, material collection, manufacture, testing and distribution. The testing technique in this research is alpha testing using black box. The final results of this study obtained the Ecopark Virtual Reality Tour Website with chat bot features, Maps, hotspots, audio narration and backsound.
Rancang Bangun Virtual Reality Tour Untuk Media Promosi Tempat Hangout Menggunakan Metode Multimedia Development Life Cycle Nurhaqiqi, Lisda; Julianto, Indri Tri
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.1688

Abstract

The culinary and tourism industry in Garut Regency is growing rapidly with the emergence of various cafes, restaurants, and tourist attractions that target visitors from various groups. Currently, marketing strategies are still dominated by conventional media such as images, videos, and text, which are less interactive and have not utilized digital technology optimally. Therefore, this research aims to design and build a Virtual Reality Tour Website as an innovative solution in the promotion of Hangout places in Garut. This website is designed to provide an interactive and immersive experience, allowing users to feel as if they are directly at the tourist attraction. The method used is the Multimedia Development Life Cycle (MDLC), which includes the stages of Concept, Design, Material Collecting, Assembly, Testing, and Distribution. Website development was carried out using 3D Vista software, with Alpha and Beta testing involving 20 respondents from five tourist attractions in Garut. The test results showed a usability testing value of 4.66, indicating a very good assessment. This website provides features such as tourist information, facilities, rides, galleries, culinary, location maps, Virtual Reality Tour, automatic chat, and profile videos. With the application of Virtual Reality technology, it is expected to increase tourist attraction and popularize Hangout places in Garut at the national and international levels.
Sistem Pendukung Keputusan Pemilihan Supplier Bahan Baku Pengadaan Material Golf Bag Custom Berbasis Simple Additive Weighting Julianto, Indri Tri; Dzulkhomzah, Moh Rival
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.1731

Abstract

Selecting the right raw material supplier is crucial for the sustainability of a company's business operations. This study focuses on the development of a web-based decision support system to optimize the supplier selection process. The Simple Additive Weighting (SAW) method was chosen due to its ease of implementation and its ability to handle multiple criteria. The system takes into account various key factors, including price, delivery speed, type, discount, quality, service, warranty, payment terms, and return policy. Evaluation results show that the developed decision support system is capable of providing more accurate and objective supplier recommendations compared to the previously used manual method. Therefore, this system is expected to improve the efficiency of raw material procurement and support better decision-making.
Rancang Bangun Media Pembelajaran Interaktif Materi Operasi Hitung Untuk Sekolah Dasar Berbasis Android Rahayu, Raden Erwin Gunadhi; Julianto, Indri Tri; Nurul Muttaqin, Epwan
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.1851

Abstract

This research aims to design and develop android-based interactive learning media to help grade 1 elementary school students understand addition and subtraction operations. Multimedia Development Life Cycle (MDLC) includes the stages of concept, design, material collecting, assembly, testing, and distribution. The learning media is built using Adobe Animate and equipped with gamification elements such as badges, countdown, and score to increase student engagement. Alpha and beta testing were conducted on users, with beta testing results showing a user satisfaction score of 87.4% based on a Likert scale. The results show that the developed interactive application is feasible to be used as an alternative to learning mathematics in elementary schools.
Co-Authors Abdullah, Angga Abdulrohman, Muhammad Haviz Ade Sutedi Ade Sutedi, Ade Aditriyana, Muhammad Rizky Agisni Nurlela, Agni Agreindra Helmiawan, Muhammad Akhdan Hidayat, Fairuz Alamsyah, Restu Alisha Fauzia, Fathia Apriliani, Insani Ardana, Alwan Arif Rahman, Rifal Arif Syamsudin, Muhammad Asri Mulyani B. Balilo Jr , Benedicto B. Balilo Jr, Benedicto Balilo Jr, Benedicto B. Baswardono, Wiyoga Burhanudin, Asep Chaerunisa, Adinda Citra Indahsari, Ajeng Dede Kurniadi Deni Heryanto, Deni 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 Haris, Gendhi 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 Nurpajar, Dini Siti 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 Ibrahim, Maulana Ridwan Setiawan Rinda Cahyana Rinda Cahyana Rohman, Fauza Rohmanto, Ricky Rusdiawan, Mohamad Mihradi Rustandy, Sandy Sadikin, M. Fitroh Saepul Jamil, Alwis Sanusi, Aini Fauziah Putu Septian Rheno Widianto Sermana, Elsa Maharani Setiawan Putra, Achmad Dhani Sidqi, Muhammad Affan Al Sirojudin, Naufal Suryadi, Khaila Thsabita Suryani, Isma Taupik Hidayat, Taupik Tizani, Sofyan Tria Afini Ujang Sarifudin Yoga Handoko Agustin Yosep Septiana