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All Journal JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Informatika Upgris Jurnal Khatulistiwa Informatika JurTI (JURNAL TEKNOLOGI INFORMASI) JUTIM (Jurnal Teknik Informatika Musirawas) Informatika : Jurnal Informatika, Manajemen dan Komputer JUSIM (Jurnal Sistem Informasi Musirawas) Jurnal Informatika Kaputama (JIK) EVOLUSI : Jurnal Sains dan Manajemen JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Journal of Computer Networks, Architecture and High Performance Computing JUSTIAN - Jurnal Sistem Informasi Akuntansi Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Indonesian Community Service Journal of Computer Science (IndoComs)
Weiskhy Steven Dharmawan
Universitas Bina Sarana Informatika
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Environmental Science Library & Information Science Other

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Rancang Bangun Sistem Informasi Akuntansi Arus Kas (SIAKAS) Berbasis Web Pada PD. Afiat Prima Dharmawan, Weiskhy Steven; Ardiyansyah
Jurnal Sistem Informasi Akuntansi Vol. 5 No. 2 (2024): Periode September 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/justian.v5i2.7753

Abstract

Arus kas merupakan suatu bagian laporan keuangan yang telah disajikan untuk memberikan suatu informasi terkait dengan kondisi keuangan perusahaan pada suatu periode tertentu serta dapat berguna bagi berbagi pihak diantarnya manajemen, pihak eksternal serta bagi internal perusahaan itu. Penelitian ini bertujuan untuk mengatasi masalah yang timbul dan mempermudahkan dalam pengolahan data serta pembuatan laporan pada PD.Afiat Prima. Metode penelitian pengumpulan data yang dilakukan yaitu dengan cara observasi, wawancara dan studi pustakan sedangkan model pengembangan system yang digunakan adalah Waterfall Model .”Waterfall Model sendiri adalah rangkaian alur pengembangan system yang di mulai dari planning, analysis,design, implementation, operation & maintenance”. Hasil dari penelitian ini adalah perusahaan sudah bisa menggunakan system terkomputerisasi yang dapat mempermudahkan dalam pengolahan data dan pembuatan laporan sehinggan pekerjaan yang di lakukan lebih efisien
PENERAPAN SISTEM INFORMASI AKUNTANSI DALAM MENGELOLA TRANSAKSI KEUANGAN PADA BUMDES USAHA BERSAMA KABUPATEN SINTANG Steven Dharmawan, Weiskhy; Yulia, Yulia; desiyana, fransiska
Jurnal Sistem Informasi Akuntansi Vol. 4 No. 2 (2023): Periode September 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/justian.v4i2.2917

Abstract

Technology and information are currently experiencing developments, automatically demanding that all areas of activity be computerized, one of which is in the field of accounting. BumDes Joint Enterprises of Gemba Raya Village, Kelam Permai District, Sintang Regency is a village-owned business institution engaged in the sale of agricultural products, namely the sale of fertilizers and grass poison. The problem faced by BumDes Joint Ventures is the processing of financial data which is still manual using bookkeeping records including in the preparation of financial reports such as collecting transaction evidence processing data, recording general journals, recording general ledgers, recording balances, recording adjusting journals, recording work balances and ending with financial reports (balance sheets, profit/loss reports, and reports on changes in capital). For this reason, the authors recorded financial transactions using Zahir Accounting software version 6.0 on the BumDes Joint Ventures of Sintang Regency. Using Zahir Accounting version 6.0 can provide a solution for BumDes Usaha Bersama in making financial reports more effective and efficient so that they can support financial transaction activities at this institution.
Pemanfaatan Power Point dalam Pembuatan Kartu Ucapan pada Pondok Asuhan dan Pendidikan Yatim Piatu Dhuafa Al-Adabiy Pontianak Dedi Saputra; Weiskhy Steven Dharmawan; Deni Risdiansyah; Muhamad Syarif
Indonesian Community Service Journal of Computer Science Vol. 1 No. 1 (2024): Periode Januari 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/indocoms.v1i1.2263

Abstract

Perkembangan teknologi yang begitu pesatnya telah memberikan berbagai perubahan dalam bidang kehidupan khususnya dalam dunia pendidikan. Salah satu contoh nyatanya yang dapat kita lihat yaitu dalam proses mengajar yang dilakukan guru-guru yang mana sebagian besar telah menggunakan teknologi sebagai sarana mengajar. Sehingga dengan kemajuan teknologi seperti ini dibutuhkan suatu badan kerjasama yang bertujuan untuk memajukan kualitas mengajar, seperti hal nya penggunaan sebuah Software yang dapat membatu dalam sistem pengajaran. Salah satu alat teknologi yang saat ini banyak dimanfaatkan dalam dunia pendidikan adalah hadirnya Microsoft Office PowerPoint, Software ini adalah sebuah program komputer untuk presentasi yang dikembangkan oleh microsoft. Microsoft Office, selain Microsoft Word, Excel, Access dan beberapa program lainnya. Fiturnya yang pengoperasiannya yang mudah dimengerti menjadikannya sebagai pilihan oleh siapapun yang ingin membuat media pembelajaran interaktif dengan cepat dan mudah. Dibandingkan dengan aplikasi yang lain, PowerPoint lebih lengkap dan praktis dalam penampilan video, gambar, animasi dan juga slide.
Pemodelan Klasifikasi Kategori Harga Rumah Menggunakan Algoritma Decision Tree dengan Pendekatan CRISP-DM Ghaisani, Bunga Aulia; Damayanti, Griselda Audrye; A. Rani, Rizki Mautya; Fitri, Nur; Dharmawan, Weiskhy Steven
Jurnal Informatika UPGRIS Vol 11, No 2 (2025): DESEMBER 2025
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jiu.v11i2.25421

Abstract

Prediksi harga rumah yang akurat merupakan informasi krusial bagi berbagai pemangku kepentingan di pasar properti, termasuk pembeli, penjual, dan investor, untuk pengambilan keputusan strategis. Penelitian ini bertujuan untuk merancang dan mengimplementasikan model machine learning menggunakan Algoritma Decision Tree, yang dikenal karena kemudahan interpretasinya, untuk mengklasifikasikan harga rumah ke dalam tiga kategori: rendah, sedang, dan tinggi. Metodologi yang diterapkan mengadopsi kerangka kerja CRISP-DM (Cross-Industry Standard Process for Data Mining), yang meliputi tahapan pemahaman data, persiapan data, pemodelan, evaluasi, hingga deployment. Algoritma Decision Tree digunakan sebagai model prediktif utama, dan kinerjanya diukur menggunakan metrik akurasi klasifikasi. Sebagai tahap akhir, model yang telah dilatih sistem aplikasi web interaktif menggunakan Streamlit, sebuah framework modern berbasis Python yang mempercepat proses deployment. Hasil penelitian menunjukkan bahwa model Decision Tree mampu mengklasifikasikan kategori harga rumah dengan tingkat akurasi sebesar 81,74%. Implementasi menggunakan Streamlit terbukti berhasil menyediakan antarmuka yang efektif, intuitif, dan praktis, memungkinkan pengguna non-teknis untuk berinteraksi langsung dengan model prediktif.
Pemanfaatan Framework Codeigniter Dalam Pembuatan Sistem Informasi Bimbel Bahasa Inggris Berbasis Web Weiskhy Steven Dharmawan; Ardiyansyah Ardiyansyah
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 3 No. 2 (2021): Desember 2021
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i2.3611

Abstract

The Pontianak English Village Tutoring Center is one of the institutions engaged in English education. The system applied still uses book media for recapitulation or report presentation. The problem that becomes an obstacle for the English village is that the admin who processes the data often makes mistakes and delays in recording and making reports. Therefore, this study describes the creation of a web-based English tutoring information system using the Codeigniter framework in assisting the required information system. In addition, in this study the author uses a descriptive method and the waterfall model. This information system consists of three users, namely: admin, student, and owner. Where the admin can manage all menu data consisting of registration confirmation, student payments, schedule input, classes. Students register, pay, and re-register. While the owner sees the report data that has been inputted. This web-based tutoring information system is expected to help Pontianak English Village in recording all existing activities and students who want to register do not need to bother coming to the place, only using mobile phones can register and make payment transactions using the website
Performance Evaluation of the BERT Model in Sentiment Analysis of DANA Application User Reviews Hazael Susanto; Weiskhy Steven Dharmawan; Riski Annisa; Lady Agustin Fitriana
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2359

Abstract

The rapid growth of digital wallets in Indonesia generates a large volume of user reviews on platforms such as the Google Play Store that cannot be efficiently analyzed manually. This study aims to evaluate the performance of the BERT (Bidirectional Encoder Representations from Transformers) model in sentiment classification tasks on a dataset of DANA application user reviews collected from the Google Play Store. The BERT model is fine-tuned using labeled Indonesian-language data with three sentiment classes: positive, negative, and neutral. Specialized preprocessing strategies are applied to handle the characteristics of informal text, abbreviations, and code-switching phenomena prevalent in Indonesian user reviews. Evaluation is conducted using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that the fine-tuned IndoBERT model achieves an accuracy of 91.24% with a weighted F1-score of 0.91 on a test dataset of 6,106 samples. The Negative class achieves the highest performance with an F1-score of 0.95, followed by the Positive class (0.88) and Neutral class (0.84). This study provides empirical evidence of the effectiveness of the IndoBERT Transformer architecture for sentiment analysis in the Indonesian-language fintech domain and can serve as a reference for developing deep learning-based NLP systems in similar contexts.
Performance Evaluation of Machine Learning Algorithms in Sentiment Analysis of Spotify Reviews Frizi Olivian; Sahrul Bariyah; Grant Christo Budiyanto; Riski Annisa; Lady Agustin Fitriana; Weiskhy Steven Dharmawan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2362

Abstract

The rapid growth of digital music streaming platforms has generated a massive volume of user reviews on the Google Play Store, making manual analysis practically infeasible. This study evaluates and compares the performance of three machine learning algorithms Support Vector Machine (SVM), Neural Network (Multilayer Perceptron), and Random Forest in classifying sentiments from Spotify user reviews written in Indonesian. A total of 10,000 reviews were collected from the Google Play Store using the google-play-scraper library and processed through a text preprocessing pipeline comprising cleaning, case folding, word normalization, tokenization, stopword removal, and stemming using the Sastrawi library. Sentiment labeling was performed automatically using the InSet lexicon, categorizing reviews into three classes: Positive (56.63%), Neutral (30.60%), and Negative (12.76%). Feature extraction was conducted using the TF-IDF method, with an 80:20 train-test split strategy and stratified sampling to maintain class distribution. Model performance was evaluated based on accuracy, precision, recall, and F1-score metrics. The results demonstrate that SVM and Neural Network achieved equivalent and superior accuracy of 0.937, with macro F1-scores of 0.908 and 0.907, respectively, outperforming Random Forest which recorded an accuracy of 0.853 and a macro F1-score of 0.777. These findings indicate that SVM and Neural Network are more optimal and reliable for sentiment classification of Indonesian-language Spotify reviews, while Random Forest requires further improvement, particularly in recognizing minority classes.
Topic Modeling of Clash of Clans Player Reviews Using NLP-Based Latent Dirichlet Allocation (LDA) Machine Learning Method Rai Markus Panamuan; Debi Handika; Muhamad Rizki Pratama; Weiskhy Steven Dharmawan; Lady Agustin Fitriana; Riski Annisa
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2364

Abstract

The rapid growth of the mobile gaming industry has generated millions of player reviews on platforms like the Google Play Store. Clash of Clans, developed by Supercell, is one of the world's most popular mobile strategy games, generating a vast volume of user reviews that are difficult to analyze manually. This study applies Latent Dirichlet Allocation (LDA), a generative probabilistic machine learning model based on Natural Language Processing (NLP), to identify and cluster key topics discussed in player reviews on the Google Play Store. A total of 10,000 player reviews were collected through web scraping, followed by NLP-based text preprocessing including tokenization, stopword removal, and lemmatization. The LDA model was optimized using a coherence score evaluation of 0.512, resulting in the identification of five dominant discussion topics: technical issues and bugs, game updates and balance, gameplay and strategy, monetization and in-app purchases, and social interactions and clan systems. The results show that LDA-based topic modeling provides structured and actionable insights for game developers to understand player feedback and improve game quality. This research contributes to the field of NLP-based mobile game review analysis.
Perbandingan Algoritma Machine learning Untuk Analisis Sentimen Aplikasi Instagram Berbasis NLP Yuridis Seri Rahmat; Saskia Renata; Albertus Belo; Weiskhy Steven Dharmawan; Lady Agustin Fitriana; Riski Annisa
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.1057

Abstract

Instagram merupakan salah satu platform media sosial terbesar di dunia dengan lebih dari dua miliar pengguna aktif bulanan. Ulasan pengguna di Google Play Store menjadi sumber data yang kaya untuk memahami persepsi dan kepuasan pengguna terhadap fitur serta layanan yang diberikan. Penelitian ini bertujuan menganalisis sentimen ulasan pengguna aplikasi Instagram menggunakan tiga algoritma klasifikasi machine learning, yaitu Support Vector Machine (SVM), Random Forest (RF), dan Neural Network (NN), dengan representasi fitur menggunakan metode Bag-of-Words (BoW). Data yang dikumpulkan sebanyak 10.000 ulasan melalui teknik web scraping dari Google Play Store menggunakan library google-play-scraper, dan setelah melalui proses preprocessing diperoleh 7.236 data bersih yang digunakan untuk pemodelan. Pelabelan data dilakukan secara otomatis menggunakan leksikon InSet (Indonesia Sentiment Lexicon). Tahapan preprocessing meliputi pembersihan teks, case folding, normalisasi kata tidak baku, tokenisasi, stopword removal, dan stemming menggunakan algoritma Sastrawi. Hasil eksperimen menunjukkan bahwa SVM memperoleh akurasi tertinggi sebesar 90,7%, diikuti Neural Network 89,2%, dan Random Forest 81,5%. Berdasarkan hasil tersebut, SVM terbukti paling unggul dalam mengklasifikasikan sentimen positif, negatif, dan netral pada ulasan pengguna aplikasi Instagram.
Co-Authors A. Rani, Rizki Mautya Albertus Belo Andi Saputra Ardiyansyah Ardiyansyah Ardiyansyah Ardiyansyah Ardiyansyah Ardiyansyah Ardiyansyah Baehaqi Chika Suhendra Damayanti, Griselda Audrye Debi Handika Dedi Saputra desiyana, fransiska Dharmawan, Weiskhy Frizi Olivian Ghaisani, Bunga Aulia Grant Christo Budiyanto Hazael Susanto Heni Wardani Lady Agustin Fitriana Lady Agustin Fitriana Lisnawanty Muhamad Rizki Pratama Muhamad Syarif Muhammad Iqbal Nur Fitri Purwaningtias, Deasy Purwaningtias, Deasy Rai Markus Panamuan Risdiansyah, Deni Riski Annisa Riski Annisa Sahrul Bariyah Saskia Renata Septian, Rendi Sri Murni Suhendra, Chika Wahyu Nugraha Yeni Mardi Yulia Yulia Yuridis Seri Rahmat