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All Journal Jurnal Sains & Teknologi Modifikasi Cuaca Jurnal Teknologi Sistem Informasi dan Aplikasi Social, Humanities, and Educational Studies (SHEs): Conference Series Building of Informatics, Technology and Science EAJ (ECONOMICS AND ACCOUNTING JOURNAL) Journal Sensi: Strategic of Education in Information System Jurnal Sistem Komputer & Kecerdasan Buatan Infotech: Journal of Technology Information Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) International Journal of Engineering, Science and Information Technology Jurnal Pengabdian kepada Masyarakat JOURNAL OF HUMANITIES, SOCIAL SCIENCES AND BUSINESS (JHSSB) International Journal of Multidisciplinary Research and Literature (IJOMRAL) Journal of Informatics and Electronics Engineering International Journal of Disaster Management Ranah Research : Journal of Multidisciplinary Research and Development Jurnal Ilmu Komputer
Tukiyat Tukiyat, Tukiyat
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Data Analysis of Thesis Guidance Students Using Random Forest, Gradient Boosting, and Naïve Bayes Algorithms (Case Study: University of Raharja) Iman, Fajar Nur; Tukiyat, Tukiyat; Taryo, Taswanda
Journal Sensi: Strategic of Education in Information System Vol 11 No 1 (2025): Journal SENSI
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sensi.v11i1.3771

Abstract

Thesis guidance is a crucial stage in higher education, as the thesis is one of the primary requirements for earning a bachelor's degree. One of the main challenges in thesis guidance is managing consultation data between students and their supervisors. The application of technology and machine learning approaches offers significant potential in addressing this issue. Machine learning algorithms such as Random Forest, Gradient Boosting, and Naïve Bayes can be utilized to automatically analyze thesis guidance data, thereby assisting supervisors in efficiently monitoring student progress. This research aims not only to provide a solution for supervisors in monitoring the progress of their students but also to offer a valuable tool for university management to evaluate the performance of supervisors in providing guidance. Based on the results and comparisons conducted, it can be concluded that the Gradient Boosting method achieves the highest accuracy, reaching 100%, compared to Random Forest with an accuracy of 98.8% and Naïve Bayes with an accuracy of 97.4%. From the testing data results using the Naïve Bayes, Gradient Boosting, and Random Forest algorithms, different accuracy levels were observed. However, the prediction outcomes were consistent: out of 235 testing data, 25 data points were classified as "Not Eligible," and 210 data points were classified as "Eligible" based on the established criteria.
Predictive Analysis of Potential Fraud in the Distribution of The Program Indonesia Pintar (PIP) Funds Using the Naïve Bayes and SVM Methods Gumay, Rizki Izandi; Anggai, Sajarwo; Tukiyat, Tukiyat
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.982

Abstract

The distribution of funds for The Indonesia Smart Program (Program Indonesia Pintar, or PIP), as a national education assistance program, faces serious challenges related to the potential for fraud that can harm the state and hinder the goal of equitable access to education. This study aims to develop a machine learning-based predictive model to detect potential fraud in the distribution of PIP funds by comparing two main algorithms, Naive Bayes and Support Vector Machine (SVM). The dataset used is the result of the integration of PIP and DAPODIK data in 2023, as well as additional features of engineering results based on the pattern of audit findings. All data, through preprocessing, normalization, and balancing processes, uses SMOTE to overcome class imbalances. The model was evaluated using accuracy, precision, recall, and F1-score metrics, both on internal and external test data from Banten Province. The results showed that SVMs with RBF kernel and optimal parameter tuning provided the best performance with an accuracy of up to 98.5% on test data. At the same time, Naive Bayes tended to be more sensitive to changes in data distribution in new data. Features such as recipient differences, budget checks, and stakeholder proposals have proven to be the leading indicators in detecting fraud. This study emphasizes the importance of external validation and regular model updates so that fraud detection systems remain adaptive to data dynamics in the field. The resulting model can be used as a tool for supervision and decision-making to prevent fraud in distributing education funds.
ANALISIS SENTIMEN OPINI PENGGUNA JASA PENGIRIMAN JNE MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER DAN K-NEAREST NEIGHBORS Halimatussadiah, Siti; Tukiyat, Tukiyat; Taryo, Taswanda
Infotech: Journal of Technology Information Vol 11, No 1 (2025): JUNI
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i1.358

Abstract

JNE's high-quality service will provide optimal satisfaction to users, ensuring they feel valued and have a reliable and efficient delivery experience. To provide optimal service, this research explores in-depth user sentiment analysis of freight forwarding applications in Indonesia. The purpose of the study is to analyze user sentiment towards the My JNE app, which is one of the leading freight forwarding apps in Indonesia. This research uses user review data from Google Play Store collected from 2018 to 2024. The review sentiment is categorized into positive, neutral, and negative using the VADER analysis tool. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool specifically designed to detect sentiment in social media text. After the data reduction process, neutral sentiment classes were removed to focus the analysis on two main categories: positive and negative. Of the total 5,000 review samples analyzed, it was found that 35.78% belonged to the positive category and 64.21% to the negative category. The classification methods used in this study are Naïve Bayes and K-Nearest Neighbors (KNN). The analysis results show that the Naïve Bayes model has an accuracy of 81.64%, while K-Nearest Neighbors (KNN) has an accuracy of 76.25%. This accuracy test confirms that the KNN model is more effective in classifying user sentiment compared to Naïve Bayes. The results of this study provide important insights into user perceptions of the My JNE application, which can be used as a basis for improving service quality in the future. This research suggests that My JNE focus on improving features that often receive negative reviews to increase user satisfaction.
Analysis of Stock Price Prediction for PT Mayora Indah Tbk Using ARIMA and Prophet Models Tukiyat, Tukiyat; Nuraini, Ani; Sembodo, Eko; Supriatna, Dahlan; Sova, Maya
JOURNAL OF HUMANITIES, SOCIAL SCIENCES AND BUSINESS Vol. 4 No. 4 (2025): AUGUST
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/jhssb.v4i4.1903

Abstract

The instability of stock market prices necessitates the utilization of precise predictive models, with ARIMA and Prophet providing alternative methods for addressing patterns, seasonal variances, and changes in value. This study aims to compare the forecasting performance of ARIMA and Prophet models in predicting the stock price of PT Mayora Indah Tbk. (MYOR.JK) using daily closing price data obtained from Yahoo Finance, spanning the period from January 1, 2018, to May 2, 2025. ARIMA was employed for its robustness in handling stationary and linear time series, whereas Prophet was applied due to its flexibility in capturing nonlinear components, seasonal fluctuations, and sudden market changes. The models were developed and evaluated in RStudio, with accuracy measured using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The ARIMA (1,1,1) model produced a MAPE of 3.21% and white noise residuals, signifying reliable short-term predictions yet limited adaptability to complex long-run dynamics. Conversely, the Prophet model achieved a lower MAPE of 2.87%, exhibiting superior predictive accuracy, trend adaptability, and sensitivity to abrupt price movements. Overall, the findings indicate that Prophet outperforms ARIMA for daily stock price forecasting and underscore the importance of selecting appropriate models in financial time series analysis, while also encouraging future exploration of hybrid or deep learning-based approaches such as Long Short-Term Memory (LSTM) networks to further enhance prediction accuracy.
Enhancing BERTopic with Neural Network Clustering for Thematic Analysis of U.S. Presidential Speeches Anggai, Sajarwo; Zain, Rafi Mahmud; Tukiyat, Tukiyat; Waskita, Arya Adhyaksa
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5090

Abstract

Understanding the underlying themes in presidential speeches is critical for analyzing political discourse and determining public policy direction.  However, topic modeling in this context presents difficulties, particularly when clustering semantically rich topics from high-dimensional embeddings.  This study seeks to improve topic modeling performance by incorporating a Neural Network Clustering (NNC) approach into the BERTopic pipeline.  We analyze 2,747 speeches delivered by U.S President Joe Biden (2021-2025) and compare three clustering techniques: HDBSCAN, KMeans, and the proposed Autoencoder-based NNC.  The evaluation metrics (UMass, NPMI, Topic Diversity) show that NNC produces the most coherent and diverse topic clusters (UMass = -0.4548, NPMI = 0.0234, Diversity = 0.3950, ).  These findings show that NNC can overcome the limitations of density and centroid-based clustering in high-dimensional semantic spaces. The study contributes to the field of Natural Language Processing by demonstrating how neural-based clustering can improve topic modeling, particularly for complex, real-world political corpora.
Narasi Presiden Indonesia: Analisis Wacana Politik Menggunakan BERTopic dalam Mengungkap Pola Tematik Pidato Presiden Uliyatunisa, Uliyatunisa; Tukiyat, Tukiyat; Waskita, Arya Adhyaksa; Handayani, Murni; Zain, Rafi Mahmud
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8298

Abstract

The speeches of the President of Indonesia play an important role as a means of political communication, policy delivery, and leadership image building in front of the public. However, the increasing volume of speeches presents new challenges in the manual analysis process, as it is time-consuming and prone to researcher subjectivity. This study offers a solution by using BERTopic, a transformer-based topic modelling method that utilises semantic representations from modern embedding models. The research data consists of transcripts of President Joko Widodo's official speeches obtained from the Cabinet Secretariat portal. To improve the quality of semantic representations, this study compares several Indonesian language embedding models, namely DistilBERT, NusaBERT, IndoE5, and SBERT. The analysis process was carried out through the stages of data preprocessing, embedding formation, dimension reduction, clustering, and model evaluation using topic coherence metrics. The objectives of this study were to reveal the themes contained in the President's speeches and to evaluate the effectiveness of embedding models in producing more coherent topics. The results show twenty main themes that consistently appear, including infrastructure development, economic policy, health and the pandemic, digital transformation, international diplomacy, sports, nationalism issues, and regional development. In terms of performance, SBERT provides the best results with a coherence value of UMass = -2.036 and NPMI = 0.082, indicating a positive semantic relationship. A UMass value close to zero indicates greater coherence of words within a topic, while an NPMI value above zero indicates that the connections between words are more easily understood by humans. This research contributes to the development of NLP-based political discourse studies in Indonesia, providing an empirical overview of the selection of appropriate embedding models in topic modelling and opening up opportunities for the integration of similar methods in public policy analysis.
Analisis Sentimen Ulasan Pengguna Aplikasi Info BMKG pada Google Play Store Menggunakan Model Transformer BERT dan RoBERTa Brando, Charlo; Anggai, Sajarwo; Tukiyat, Tukiyat
Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) Vol. 9 No. 1 (2025): Volume IX - Nomor 1 - September 2025
Publisher : Teknik Informatika, Sistem Informasi dan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47970/siskom-kb.v9i1.872

Abstract

Aplikasi Info BMKG memiliki peran penting dalam menyampaikan informasi cuaca, iklim, gempa bumi, dan peringatan dini bencana kepada masyarakat. Seiring meningkatnya penggunaan perangkat mobile di Indonesia, analisis sentimen menjadi relevan untuk mengevaluasi kepuasan pengguna serta mengidentifikasi aspek yang perlu diperbaiki. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna terhadap aplikasi Info BMKG di Google Play Store dengan memanfaatkan model transformer BERT dan RoBERTa. Dataset 10.791 ulasan pengguna yang diklasifikasikan ke dalam tiga kategori sentimen meliputi positif, netral, dan negatif. Tahapan penelitian mencakup eksplorasi data awal, prapemrosesan data, serta evaluasi model. Hasil evaluasi menunjukkan bahwa model BERT memberikan performa terbaik dengan akurasi sebesar 93,14%, disusul oleh RoBERTa dengan akurasi 91,06% pada skenario pembagian data 80:10:10. Selain itu, model BERT juga unggul dalam metrik lain, yakni presisi 93,45%, recall 92,90%, dan F1-score 93,17%, dibandingkan RoBERTa dengan presisi 91,12%, recall 90,72%, dan F1-score 90,91%. Analisis lanjutan menunjukkan bahwa meskipun aplikasi mendapatkan apresiasi, pengguna juga menyoroti isu keterlambatan notifikasi gempa dan ketidakakuratan informasi. Temuan ini diharapkan dapat menjadi dasar pengembangan lebih lanjut dalam meningkatkan kualitas layanan dan efektivitas penyampaian informasi oleh BMKG.
ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI MEDIA SOSIAL X DI PLAY STORE MENGGUNAKAN ALGORITMA LONG SHORT-TERM MEMORY (LSTM) DAN GATED RECURRENT UNIT (GRU): Studi Kasus pada Ulasan Pengguna di Google Play Store Wily, Wily Arisandi; Anggai, Sajarwo; Tukiyat, Tukiyat
Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) Vol. 9 No. 1 (2025): Volume IX - Nomor 1 - September 2025
Publisher : Teknik Informatika, Sistem Informasi dan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47970/siskom-kb.v9i1.875

Abstract

Penelitian ini bertujuan untuk membandingkan performa dua algoritma deep learning, yaitu Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU), dalam melakukan klasifikasi sentimen terhadap ulasan pengguna aplikasi media sosial X di Google Play Store. Dataset yang digunakan sebanyak 5.100 data ulasan yang telah diberi label secara manual ke dalam tiga kategori sentimen kelas positif, netral, dan negatif. Proses evaluasi dilakukan melalui 12 skenario kombinasi hyperparameter yang melibatkan variasi nilai learning rate, regularization, epoch, dan batch size. Data dibagi menjadi tiga bagian, yaitu 70% untuk pelatihan, 15% validasi, dan 15% pengujian. Hasil evaluasi menunjukkan bahwa model LSTM dengan skenario 0.002-LSTM-100-512 memberikan performa terbaik dengan akurasi 0.842, presisi 0.730, recall 0.719, dan F1-score 0.724. Sementara itu, model GRU terbaik dengan skenario 0.001-GRU-100-256 menghasilkan akurasi 0.837, presisi 0.713, recall 0.690, dan F1-score 0.696. Meskipun GRU memiliki nilai presisi yang kompetitif, model LSTM unggul dalam semua metrik lainnya, terutama F1-score yang menjadi indikator utama dalam penelitian ini karena mencerminkan keseimbangan antara presisi dan recall. Berdasarkan hasil tersebut, model LSTM dipilih sebagai model paling optimal untuk tugas analisis sentimen dalam studi ini.
Financial Contagion and Good Corporate Governance on Bank Companies Performance in Indonesian Stock Exchange Sugiyanto, Sugiyanto; Tukiyat, Tukiyat
EAJ (Economic and Accounting Journal) Vol. 4 No. 3 (2021): EAJ (Economic and Accounting Journal)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/eaj.v4i3.y2021.p164-178

Abstract

This study aims to examine the effect of fianancial contagion and good corporate governance on company performance of banks company listed on  Indonesia Stock Company. Corporate governance is measured using the number of independent commissioners, frequency of board meetings, and attendance at board meetings. This study has two dependent variables, namely market performance as measured by Price Earning Ratio (PER) and operational performance as measured by return on equity (ROE). The analysis method used is multiple regression models with two dependent variables. The results showed that the contagion effect had a positive influence on the company's PER performance but did not have an effect on the company's ROE performance. Meanwhile, corporate governance through the board of directors' meeting is able to have an influence on ROE performance but not on PER. This shows that when there is a domino effect from another country it will have an influence on share prices in the market.
Membangun Kreativitas Melalui Pelatihan Media Sosial Youtube Bagi Pengurus Anak Cabang Gerakan Pemuda Ansor Kecamatan Setu Kota Tangerang Tukiyat, Tukiyat; Makhsun, Makhsun; Hindasyah, Achmad
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 5 No. 1 (2024): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN)
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v5i1.2977

Abstract

Pada era digital saat ini, platform media sosial telah menjadi bagian tak terpisahkan dari kehidupan masyarakat. Eksistensi media sosial youtube mempunyai peran penting bagi masyarakat khususnya masyarakat generasi muda. Hal itu terlihat dari banyaknya prestasi maupun karya generasi muda yang sukses melalui media sosial youtube. PkM ini bertujuan membekali pengetahuan, pemahaman dan keterampilan bagi masyarakat dalam memanfaatkan media sosial youtube untuk meningkatkan kegiatan kreativitas dan produktif. Target dan sasaran dalam PkM adalah masyarakat Geakan Pemuda Ansor Kecamatan Setu Kota Tangerang Selatan.  Jenis kegiatan adalah pelatihan dalam membuat konten media sosial melalui media youtube. Materi pembelajaran pelatihan antara lain materi teoretis tentang konsep dan teori serta materi teknis/praktik langsung menggunakan aplikasi pengolah youtube. Metode pelaksanaan dengan ceramah, demontrasi dan praktik dalam pembuatan konten media sosial youtube. Capaian keberhasilan pelatihan diukur melalui kuesioner. Hasil analisis kuesioner menunjukkan bahwa  respon dan persepsi para peserta menilai kegiatan PkM ini baik dan sangat baik. Perlu dilakukan rencana tindak keberlanjutan PkM dalam rangka  monitoring dan evaluasi hasil karya  youtube.
Co-Authors A.A. Ketut Agung Cahyawan W Achmad Hindasyah, Achmad Agung Budi Susanto Agung Sundoro, Agung Alfan Muttaqin, Alfan Andrean, Kelvin Anggai, Sajarwo Ani Nuraini, Ani Arya Adhyaksa Waskita Brando, Charlo Budi Harsoyo Djaoharman, Sulaiman F Heru Widodo, F Heru F. Heru Widodo, F. Heru Fadhillah, Chandra Gumay, Rizki Izandi Halimatussadiah, Siti iman, Fajar nur Indrajaya, Titus Ismaya, Sakti Brata Kurnia, Muhammad Dahlan Makhsun, Makhsun Maya Sova Murni Handayani Nugraha Wahyu, Fajar Purwadi Purwadi R Djoko Goenawan, R Djoko Rafi Mahmud Zain Raharja, Yudi Rozali, Cristien Sakya, Andi Eka Satyo Nuryanto, Satyo Sembodo, Eko Sugiyanto - Sunu Tikno, Sunu Supriatna, Dahlan Supriyani, Mei Sutrisno, Sutrisno Taswanda Taryo Tri Handoko Seto, Tri Handoko Uliyatunisa, Uliyatunisa Untung Haryanto, Untung Veithzal Rivai Zainal Waskita, Arya Adhyaksa Widyasari, Lala Wily, Wily Arisandi Zain, Rafi Mahmud