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All Journal Dinamik Sistemasi: Jurnal Sistem Informasi Jurnal Ilmiah FIFO ILKOM Jurnal Ilmiah KOMPUTIKA - Jurnal Sistem Komputer Building of Informatics, Technology and Science TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Algoritma Paradigma Jurnal Ilmiah Edutic : Pendidikan dan Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Ilmiah Informatika dan Ilmu Komputer CHAIN: Journal of Computer Technology, Computer Engineering and Informatics Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science Bulletin of Artificial Intelligence Jurnal Informatika dan Rekayasa Perangkat Lunak
Aditia Yudhistira
Universitas Teknokrat Indonesia
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Klasifikasi Opini Publik Pada Isu Sosial #17+8TuntutanRakyat Menggunakan Indobert Khusnul Khotimah; Aditia Yudhistira
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10455

Abstract

The issue of “17+8 People’s Demands” that emerged within Indonesia’s socio-political dynamics has become a major topic of public discussion on social media. This viral phenomenon has generated a large volume of unstructured textual data, predominantly written in informal Indonesian and slang, thereby requiring an analytical approach capable of comprehending linguistic context more effectively. This study aims to analyze and classify social media users’ sentiments from platform X using the Twitter API. The collected texts were cleaned from noise, labeled into three sentiment categories—positive, neutral, and negative—and processed using the IndoBERT algorithm to classify the polarity of public opinion. A total of 7,936 text data were successfully obtained through a crawling process. The prepared data underwent a series of preprocessing stages before being used to evaluate the model’s performance. Overall, the evaluation results showed an accuracy of 87%. Specifically, in the aspect of class-level classification, the model demonstrated consistent performance with 90% precision, 97% recall, and an F1-score of 94%. These findings indicate the effectiveness of the IndoBERT model in accurately identifying and classifying public opinions expressed in the Indonesian language. The main contribution of this research lies in the application of a transformer-based Indonesian language model to analyze emerging social issues within digital public discourse.
Perbandingan Kinerja Model Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU) Untuk Prediksi Harga Cryptocurrency Hanif Alhakim; Aditia Yudhistira
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10062

Abstract

Extreme volatility in the cryptocurrency market poses substantial financial risks, necessitating precision forecasting systems. The limitations of conventional statistical models in capturing non-linear dynamics have prompted the adoption of deep learning approaches. This study evaluates the comparative performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures in predicting the closing prices of Bitcoin (BTC), Ethereum (ETH), and Solana (SOL). Experiments utilized historical data from January 2023 to April 2026, partitioned with an 80:20 train-test ratio using a 60-day sliding window sequence. Dropout-based regularization and Early Stopping were implemented to prevent overfitting. As an empirical contribution, this research univariately examines the trade-off between memory cell complexity (LSTM) and architectural efficiency (GRU) on high-volatility data. The results demonstrate that GRU consistently outperforms LSTM across all instruments, reducing the Mean Absolute Percentage Error (MAPE) to a range of 2.93%-4.89%. Regarding computational efficiency, the GRU architecture reduced training duration by 13.33% to 36.92% compared to LSTM. Practically, these findings recommend GRU as an effective and efficient algorithmic foundation for algorithmic trading systems and digital portfolio risk management.
Sistem Pendukung Keputusan Penentuan Marketing Di PT KASA Kabupaten Lampung Tengah Menggunakan Metode Weighted Product Adi Kurniawan Ananta; Aditia Yudhistira
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10584

Abstract

Penelitian ini bertujuan untuk menerapkan Metode Weighted Product (WP) dalam Sistem Pendukung Keputusan (SPK) untuk menentukan calon karyawan marketing di PT Karunia Alam Sentosa Abadi. Metode WP dipilih karena dapat menangani kriteria yang berbeda-beda dan memberikan bobot pada setiap kriteria sesuai dengan tingkat kepentingannya. Kriteria yang digunakan dalam penelitian ini antara lain komunikasi, kemampuan negosiasi, kemampuan analisis, kreativitas, dan pengalaman kerja. Penelitian ini menggunakan Microsoft Excel sebagai alat bantu untuk mengimplementasikan metode WP dan melakukan perhitungan bobot serta penilaian alternatif. Hasil penelitian menunjukkan bahwa metode WP efektif dalam meningkatkan kualitas seleksi dan membantu perusahaan membangun tim yang kuat dan kompetitif. Dengan demikian, PT Karunia Alam Sentosa Abadi dapat membuat keputusan yang lebih tepat dalam memilih calon karyawan marketing yang berpotensi menjadi aset berharga bagi perusahaan. Hasil penelitian juga menunjukkan bahwa alternatif 12, yaitu Supian, menjadi peringkat pertama dengan nilai tertinggi 0,07258. Penelitian ini diharapkan dapat memberikan kontribusi bagi perusahaan dalam meningkatkan kualitas seleksi calon karyawan marketing dan mencapai tujuan bisnisnya.
Penerapan Metode Simple Additive Weighting untuk Pendukung Keputusan untuk Pemilihan Siswa Berprestasi: Application of the Simple Additive Weighting Method in a Decision Support System for Outstanding Student Selection Martin, Ferryal; Yudhistira, Aditia
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2666

Abstract

Pemilihan siswa berprestasi merupakan hal penting untuk meningkatkan mutu pendidikan dan memotivasi siswa agar lebih kompetitif. Namun, proses penentuan siswa berprestasi di sekolah masih sering dilakukan secara manual dan subjektif, sehingga hasilnya kurang transparan dan rawan menimbulkan ketidakpuasan. Penelitian ini bertujuan menerapkan metode Simple Additive Weighting (SAW) untuk menentukan siswa berprestasi berdasarkan kriteria yang telah ditetapkan, dengan studi kasus di SMAN 1 Pesisir Utara. Perhitungan SAW dalam penelitian ini dilakukan dengan bantuan Microsoft Excel sebagai alat bantu pengolahan data, bukan dengan membangun sistem aplikasi. Urgensi penelitian ini terletak pada kebutuhan sekolah akan metode penilaian yang objektif, terukur, dan dapat dipertanggungjawabkan untuk mengurangi bias dalam pengambilan keputusan. Penyelesaian masalah dilakukan dengan menghitung nilai setiap alternatif siswa menggunakan langkah-langkah metode SAW di Excel. Kriteria yang digunakan meliputi nilai rapor, tingkat kehadiran, prestasi akademik/non-akademik, dan penilaian sikap. Setiap kriteria diberikan bobot sesuai tingkat kepentingannya, kemudian dilakukan normalisasi matriks dan perhitungan nilai preferensi untuk seluruh alternatif siswa di SMAN 1 Pesisir Utara. Hasil perhitungan menunjukkan bahwa siswa dengan kode A1 memperoleh nilai preferensi tertinggi yaitu 0,9. Berdasarkan hasil tersebut, dapat disimpulkan bahwa metode SAW yang diimplementasikan menggunakan Excel efektif digunakan untuk menentukan siswa berprestasi di SMAN 1 Pesisir Utara secara objektif dan transparan.
Klasifikasi Opini Publik Berbasis Aspek terhadap Affiliate Marketing di TikTok Menggunakan Fine-Tuned IndoBERT: Classification of Public Opinion on Affiliate Marketing on TikTok Based on Specific Aspects Using Fine-Tuned IndoBERT Oktafiyana, Vina; Yudhistira, Aditia
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2804

Abstract

Affiliate marketing pada TikTok berkembang pesat sebagai strategi pemasaran digital berbasis komisi yang melibatkan kreator konten dalam mempromosikan produk kepada pengguna. Aktivitas ini memunculkan berbagai opini publik terkait kualitas produk, kredibilitas kreator, harga dan promosi, pengalaman belanja, serta kualitas konten yang dibagikan melalui media sosial. Penelitian ini bertujuan menganalisis sentimen pengguna terhadap praktik affiliate marketing menggunakan pendekatan aspect-based sentiment classification (ABSC) berbasis Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT). Sebanyak 10.000 komentar TikTok berbahasa Indonesia dikumpulkan dan diproses melalui tahapan preprocessing, deduplikasi, identifikasi aspek menggunakan keyword matching dan Term Frequency — Inverse Document Frequency (TF-IDF) cosine similarity, serta pelabelan sentimen menggunakan pretrained IndoBERT. Kebaruan penelitian ini terletak pada penerapan pendekatan terintegrasi antara identifikasi aspek berbasis keyword matching dan TF-IDF cosine similarity dengan model IndoBERT yang di-fine-tune untuk klasifikasi sentimen berbasis aspek pada komentar TikTok berbahasa Indonesia terkait affiliate marketing. Pendekatan ini mampu memberikan analisis opini yang lebih kontekstual pada domain affiliate marketing yang masih jarang dibahas dalam penelitian sebelumnya. Model kemudian di-fine-tune menggunakan weighted cross-entropy loss untuk menangani ketidakseimbangan kelas pada data. Hasil pengujian menunjukkan akurasi sebesar 89,79%, weighted F1-score sebesar 89,74%, dan macro F1-score sebesar 82,29%. Evaluasi menggunakan stratified 5-fold cross-validation menghasilkan rata-rata akurasi sebesar 91,03%  
Optimizing Employee Admission Selection Using G2M Weighting and MOORA Method Yuri Rahmanto; Junhai Wang; Setiawansyah Setiawansyah; Aditia Yudhistira; Dedi Darwis; Ryan Randy Suryono
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 1 (2025): March 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i1.8224

Abstract

An objective and effective employee admission selection process is a crucial step for the success of the organization in achieving its goals. Problems in employee recruitment selection often arise due to a lack of good planning and system implementation, namely decisions are often influenced by personal preferences, stereotypes, or non-relevant factors, thus reducing objectivity in choosing the best candidates. Objective selection ensures that candidate assessments are conducted based on measurable, relevant, and bias-free criteria, so that only individuals who truly meet the company's needs and standards are accepted. The purpose of developing an optimal approach in employee admission selection using G2M weighting and MOORA is to create a more objective, efficient, and accurate selection process. This approach aims to integrate the calculation of criterion weights mathematically, such as those offered by G2M, in order to eliminate subjective bias in determining criterion prioritization. The MOORA method of evaluating alternative candidates is carried out through ratio analysis that takes into account various criteria simultaneously, resulting in a transparent and data-driven ranking. The results of the employee admission selection ranking based on the criteria that have been evaluated, Candidate 3 obtained the highest score of 0.4177, indicating that this candidate best meets the expected criteria. The second position was occupied by Candidate 6 with a score of 0.3886, followed by Candidate 9 with a score of 0.3528. This research contributes to the recruitment process, by providing a more reliable, transparent, and less subjective way of selecting the right candidates for the positions that companies need.
Multi-Criteria Decision Model for Ranking the Best Marketplace Using CRISUS Weighting and OPARA Ranking Akil Thalib; Ayuni Asistyasari; Yosep Nuryaman; Raditya Rimbawan Oprasto; Aditia Yudhistira
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Volume 7 Number 1 March 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i1.1568

Abstract

The rapid growth of e-commerce marketplaces in Indonesia has increased competition among platforms and created challenges in identifying the most suitable marketplace for users and businesses. Previous studies commonly applied conventional Multi-Criteria Decision-Making (MCDM) approaches, yet many of these methods rely heavily on subjective weighting or limited data-based evaluation, which may lead to inconsistent ranking results. Therefore, this study aims to develop a more objective decision-making model for marketplace evaluation by integrating the CRISUS weighting method with the OPARA ranking approach. The dataset consists of quantitative marketplace performance indicators collected from public digital statistics, including monthly visits, annual visits, application ratings, number of downloads, and the number of active sellers for several major marketplaces operating in Indonesia. The CRISUS method is used to determine criterion weights based on actual data variation to reduce subjective bias, while OPARA evaluates the alternatives through an optimized pairwise ratio mechanism to obtain the final preference values. The experimental results indicate that Shopee achieves the highest score of 0.3078, followed by Lazada with 0.2476 and Tokopedia with 0.2327, demonstrating their stronger performance compared with other marketplace alternatives based on the evaluated criteria. These findings contribute both academically and practically by providing a transparent and data-driven MCDM framework that improves the reliability of marketplace ranking and can support stakeholders in making more informed platform selection decisions.
Perbandingan Naïve Bayes dan Support Vector Machine Berbasis Term Frequency−Inverse Document Frequency pada Analisis Sentimen Ulasan Produk Afiliasi Lintas Platform TikTok dan Shopee Clara Indriani Putri; Aditia Yudhistira
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9454

Abstract

The growth of affiliate marketing on digital platforms, particularly TikTok and Shopee, has led to a rapid increase in consumer reviews that can be leveraged as actionable insights for businesses. However, reviews across platforms exhibit different linguistic characteristics: Shopee reviews tend to be more repetitive and transactional, whereas TikTok reviews are more informal, rich in slang, and noisier. This difference creates a research gap because sentiment classification performance may vary across platforms, while comparative studies on cross-platform affiliate reviews remain limited. This study aims to analyze and compare the performance of Multinomial Naïve Bayes and Support Vector Machine in identifying positive and negative sentiment polarity in TikTok and Shopee affiliate product reviews. Data were collected via web scraping during December 2025–January 2026, yielding 5,502 raw reviews. After text preprocessing (case folding, regex-based cleaning, normalization, stopword removal, and stemming using Sastrawi), 4,593 clean reviews were obtained. Lexicon-based automatic labeling with negation handling produced a binary dataset of 3,314 reviews (2,729 positive and 585 negative), indicating class imbalance; therefore, no data balancing was applied and evaluation emphasized precision, recall, and F1-score in addition to accuracy. Feature representation used Term Frequency–Inverse Document Frequency, and the dataset was split using an 80:20 hold-out scheme (2,651 training and 663 testing instances). Experimental results show that the Support Vector Machine achieved higher performance (95.93% accuracy; 0.81 negative-class F1) than Multinomial Naïve Bayes (89.14% accuracy; 0.12 negative-class F1). This superiority is related to the ability of Support Vector Machine to learn a maximum-margin hyperplane in the high-dimensional and sparse Term Frequency–Inverse Document Frequency feature space, making it more robust to linguistic variation and noise than the probabilistic Naïve Bayes approach, which is more sensitive to majority-class dominance.
Co-Authors Adamansyah, Eka Putri Adi Kurniawan Ananta Ahmad Ari Aldino Akil Thalib Anggoro, Bayu Ari Sulistiyawati Arshad, Muhammad Waqas Asistyasari, Ayuni Clara Indriani Putri Dedi Darwis Gustina, Intan Hafizhah, Hafizhah Handika, Tegar Dio Hanif Alhakim Hari Agung Adrianto Imas Sukaesih Sitanggang Iryanto Chandra Junhai Wang KHADAFI, MUHAMMAD Khusnul Khotimah Luthfi Falih, Yudep Rafidal Ma'rufudin Mahmudah, Siti Anisa Maria Angelina Luisa Makin Martin, Ferryal Megawaty, Dyah Ayu Nasrul, Royhan niswatun umami, Nila Oktafiyana, Vina Palupiningsih, Pritasari Pasaribu, A. Ferico Octaviansyah Raditya Rimbawan Oprasto Ryan Randy Suryono Saidina Ali Habib Sanjaya, M. Alung Setiawansyah Setiawansyah Sitna Hajar Hadad Subhan Subhan Suci Fitrianti, Suci Sumanto Sumanto Tri Widodo Ulum, Faruk Wahyudi, Agung Deni Wang, Junhai Yansyah, Dery yasin, ikbal Yosep Nuryaman Yuri Rahmanto