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All Journal Jurnal Riset Informatika JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Ilmu Komputer (JIK) Jurnal Pengabdian Masyarakat Nasional
Pungkas Budiyono
Universitas Bina Sarana Informatika
Computer Science & IT Decision Sciences, Operations Research & Management Materials Science & Nanotechnology

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Sentiment Analysis User Ajaib Application Using Naïve Bayes Algorithm Suwanda Aditya Saputra; Beni Rahmatullah; Pungkas Budiyono
Journal of Information System, Informatics and Computing Vol 6 No 2 (2022): JISICOM: December 2022
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v6i2.964

Abstract

There are many advantages if everyone can invest in stock investments that are well known to the public. To facilitate and increase the interest of investors in storing shares in securities, a technology application is needed to assist investment transactions. Ajaib Application is one way to invest in both stocks and mutual funds. To provide information to the public, it is necessary to have a sentiment analysis on how the opinion of Ajaib application users uses the Naïve Bayes method with the results of scraping reviews on Google Play as many as 400 reviews. then the text processing stage is carried out to the classification stage, so that it gets an accuracy value of 89.00%, a recall value of 93.50%, a precision value of 86.36%, and an AUC value of 0.570 and the top 5 words include application words 186 repeated, magic words 77 repeated, good words 76, investment words 72 repeated and 72 easy words repeated. From the results, it can be said that there are more positive sentiments than negative sentiments in the Ajaib Application.
Audit Keamanan Sistem Informasi Jairngan Komputer Dengan COBIT 4.0 di Diskominfo Kota Tangerang Pungkas Budiyono, S.Kom., M.Kom.; Dani Ramdani, S.Kom., M.T.
Jurnal Ilmu Komputer Vol 3 No 3 (2020): Jurnal Ilmu Komputer (JIK)
Publisher : LPPM-STMIK Pranata Indonesia

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Abstract

Perkembangan TIK memberikan banyak manfaat dan keuntungan serta kemungkinan kerugian bagi suatu organisasi. Informasi sebagai salah satu sumber daya yang sangat penting dituntut untuk mudah diaskses sekaligus memerlukan pengelolaan keamaan sistem secara baik dan benar. Audit keamanan informasi diperlukan untuk mengetahui dan menjamin sistem informasi yang baik. COBIT 4.0 dapat digunakan sebagai salah satu tool evaluasi keamanan sistem informasi Diskominfo Pemerintah Kota Tangerang.
SENTIMEN ANALISIS TRANSPORTASI ONLINE MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE, NAIVE BAYES DAN KNN Beni Rahmatullah; Pungkas Budiyono; Suwanda Aditya Saputra
Jurnal Ilmu Komputer Vol 4 No 2 (2021): Jurnal Ilmu Komputer (JIK)
Publisher : LPPM-STMIK Pranata Indonesia

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Abstract

Transportasi merupakan kebutuhan yang paling digunakan dalam aktifitas sehari-hari dalam bekerja,berbelanja dan kegiatan yang lainnya. Transportasi online menjadi pilihan masyarakat. Aplikasi seperti Gojek danGrab saat ini yang paling banyak diminati. Saran dan kritik dari pelanggan guna memperbaiki sistem dan pelayananyang diberikan. Dalam hal ini penulis memperoleh data dari komentar di twitter dan menggunakan algoritma NaiveBayes, Support Vector Machine dan K-Nearest Neighbor sebagai perbandingan akurasi yang dihasilkan. Ketigaalgoritma tersebut menggunakan salah satu fitur seleksi Particle Swarm Optimization. Hasil akurasi yang didapatmembuktikan dengan fitur seleksi Particle Swarm Optimization lebih tinggi dan akurasi yang paling tinggi olehAgoritma Support Vector Machine (PSO) dengan Akurasi 98 % dan AUC 0.988.
Sistem Pendaftaran Peserta Didik Baru (PPDB) Berbasis Desktop Pada Tingkat SMP/MTS dan SMU/SMK/STM Pungkas Budiyono; Beni Rahmatullah; Suwanda Aditya Saputra
Jurnal Ilmu Komputer Vol 5 No 1 (2022): Jurnal Ilmu Komputer (JIK)
Publisher : LPPM-STMIK Pranata Indonesia

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Abstract

Pada pendaftaran peserta didik baru disetiap sekolahan pada dasarnya memiliki model yang sama .akan tetapi pada proses yang dilakukan sangatlah bervariasi atau berbeda. Mulai dari proses yang dilakukan secara manual sampai proses yang dilakukan dengan menggunakan bantuan program komputer, yang kemampuan masing-masing program atau softwarenya tentu memiliki metode yang berbeda. Dengan Adanya sistem yang diusulkan pada penelitian ini akan memudahkan para peserta didik baru dalam proses pendaftaran dan mengetahui terlebih dahulu alur proses inputnya yang meliputi “Input Data Orang Tua, Data Siswa, Asal Sekolah, Daftar Ulang, Riwayat Pendidikan, Nilai Mata Pelajaran, dan Sekolah Yang Dituju”. Maka Pada penelitian ini kami berusaha mencoba untuk membuat Sistem Pendaftaran Peserta Didik Baru Berbasis Desktop dengan menggunakan Bahasa pemrograman java.
PELATIHAN E-COMMERCE DAN MEMBUAT TOKO ONLINE PADA ASRAMA YATIM DAN DHUAFA AL ALIF 165 Ahmad Jurnaidi Wahidin; Tiska Pattiasina; Dudi Duta Akbar; Pungkas Budiyono
PEMANAS: Jurnal Pengabdian Masyarakat Nasional Vol 1, No 1 (2021)
Publisher : PEMANAS: Jurnal Pengabdian Masyarakat Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/pemanas.v1i1.13746

Abstract

The COVID-19 pandemic which was designated as a national disaster on March 14, 2020 made many sectors and lines of life weakened including trade, one of which was affected was the Asrama Yatim and Dhuafa Al Alif 165 Pondok Rajeg, Cibinong, Bogor Regency, this dormitory has trading activities such as selling cassava chips, pastries and work on trellis orders, but the sales process is still done manually, which is only trading on the side of the road in front of the hostel. The existence of a pandemic plus government regulations regarding PPKM greatly affected the mobility of the community, including in the dormitory area, so that there were very few buyers which caused the income of the Asrama Yatim and Dhuafa Al Alif 165 to decrease. By holding one-day training online with the theme of e-commerce and making a shop on the shopee application, foster children and hostel administrators can expand the market with a global reach and can transact in cyberspace without being hindered by location and conditions. This activity has made a positive contribution, namely increasing knowledge and expertise in the field of information technology in order to increase product sales. New knowledge about selling products using shopee. The training participants already know and practice how to create a shop, upload products, determine product categories to determine store locations and delivery services on shopee. Furthermore, training with the same theme can be given again but using a different marketplace such as Tokopedia, Lazada, Bibli and others.
BITCOIN PRICE VOLATILITY ANALYSIS: A DEEP LEARNING APPROACH TO X (FORMERLY TWITTER) SENTIMENT Puji Astuti; Rangga Sidiq Endrasmoyo; Syawalluddin; Yesi Fitria; Pungkas Budiyono
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1039.697 KB) | DOI: 10.34288/jri.v8i1.432

Abstract

This study investigates the relationship between social media sentiment and Bitcoin price volatility using advanced natural language processing techniques. We collected X data from April 10-29, 2025, analyzing cryptocurrency-related tweets alongside Bitcoin price movements obtained through the CoinGecko API. Five sentiment analysis methodologies were comparatively evaluated: VADER, TextBlob, BERTweet, RoBERTa Base, and RoBERTa Large. Bitcoin price volatility was measured using log returns to capture market fluctuations accurately. Correlation analysis revealed significant differences in methodological effectiveness. Traditional lexicon-based approaches (VADER and TextBlob) demonstrated weak correlations with volatility (r = -0.2232 and r = -0.0710 respectively). Transformer-based models showed superior performance, with RoBERTa Large achieving the strongest correlation (r = 0.4569, p = 0.0428), representing the only statistically significant relationship. The positive correlation indicates that increased social media sentiment corresponds to higher Bitcoin price volatility rather than directional price movements. These findings demonstrate that sophisticated deep learning models can effectively capture sentiment-driven market dynamics, providing valuable insights for cryptocurrency investors, trading platforms, and market analysts seeking to understand social media influence on digital asset markets.
Co-Authors Ahmad Jurnaidi Wahidin Akbar, Dudi Duta Beni Rahmatullah Dani Ramdani, S.Kom., M.T. Puji Astuti Rangga Sidiq Endrasmoyo Suwanda Aditya Saputra Syawalluddin Tiska Pattiasina Yesi Fitria