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Anggai, Sajarwo
Universitas Pamulang
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Analisis Dan Implementasi Sistem Manajemen Keamanan Informasi Menggunakan ISO/IEC 27001 (Studi Kasus Pada PT.XYZ) Wibowo, Rizki Septiyanto; Tukiyat; Sajarwo Anggai; Winarni
Jurnal Ilmu Komputer Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024)
Publisher : Universitas Pamulang

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Abstract

It has become a current necessity in every company regarding the implementation of information and communication technology governance in efforts to improve service quality. The implementation of information and communication technology governance is a critical factor in enhancing service quality across various companies. Therefore, the adoption of an Information Security Management System (ISMS) based on the ISO 27001:2013 standard becomes essential, in line with the conduct of regular audits to ensure its effectiveness. This research aims to develop and design an information security governance framework in accordance with ISO/IEC 27001 and to conduct audits on the system that has been implemented in PT. XYZ, to ensure its compliance with good and efficient standards. The methodology used is Plan-Do- Check-Act (PDCA), with data collection techniques through interviews and distribution of questionnaires for internal audits. The research findings indicate that the average ISO/IEC 27001 maturity level is at levels three and four. It is expected that this research can assist and provide recommendations related to security controlsused as guidelines and procedures for the implementation of information security, as well as ensuring the overall operation runs in accordance with ISO 27001 standards.
Optimizing Learning Rate, Epoch, and Batch Size in Deep Learning Models for Skin Disease Classification Rahman, Taufiqur; Anggai, Sajarwo; Arya Adhyaksa Waskita
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Abstract

This study explores the best combination of learning rate, number of epochs, and batch size for training deep learning models to classify skin diseases. The experiments involved analyzing how loss changes with learning rates on a logarithmic scale. The findings reveal that a learning rate of approximately 10-2 is most effective, with 5×10−3 offering additional stability during training. Various combinations of epochs and batch sizes were tested, ranging from 20 to 100 epochs and batch sizes between 32 and 128. The results show that using a batch size of 32 yielded the best outcomes, achieving a validation accuracy of 97.35% and the lowest validation loss of 0.1074. While a batch size of 128 was more efficient in terms of time, it resulted in slightly lower accuracy. The model performed optimally with 25 epochs and a batch size of 32, avoiding any signs of overfitting. Data preparation also played a crucial role, involving steps like image resizing, pixel normalization, and data augmentation to align with the requirements of models such as VGG-19, Inception-V4, and ResNet-152. Visualizing the dataset distribution ensured data quality and class balance, allowing the model to better recognize patterns. This study offers practical insights for effectively and efficiently training deep learning models, particularly for tasks related to skin disease classification.
Analisis Topik Penelitian Pendidikan Matematika Di Indonesia Dengan Menggunakan Metode Latent Dirichlet Allocation (LDA) junedi, Beni; Agung Budi Susanto; Sajarwo Anggai
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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On the research topic of Mathematics Education readers or researchers still have difficulty identifying research topics in the field of Mathematics Education. This is because there is no system or model that can be seen or used in determining research topics. Besides that, there is no automation of the research direction of Mathematics Education in Indonesia using topic modeling, so it is necessary to conduct a study or research on this. In research, the most important thing is the trend of research that is currently developing so that it can determine the novelty of the studies that have been done before. While there is no system used to determine trends and state of the art from research in the field of Mathematics Education. The aim of the research is to find out an overview of the research topics in Mathematics Education in Indonesia in 2020-2023 and to find out the implementation of modeling research topics in Mathematics Education in Indonesia using the Latent Dirichlet Allocation (LDA) method for 2020-2023. The research design consisted of literature study, data collection, data pre-processing: tokenization, case folding, stopword removal, and stemming, topic analysis with LDA, evaluation of the LDA method, and conclusions. Analysis of Topic Modeling with Latent Dirichlet Allocation using packages used from python including the Gensim and pyLDAvis packages. Based on the coherence score, the best number of topics (K) = 18, with a coherence score = 0.426 (the highest), it can be concluded that the number of topics produced is 18 topics.
Analisis Eksperimental Kinerja Transformers, VADER, dan Naive Bayes dalam Analisis Sentimen Teks Bahasa Indonesia: Studi Kasus Komentar Terkait Judi Online Sugiyo; Agung Budi Susanto; Sajarwo Anggai
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Sentiment analysis is a subfield of Natural Language Processing (NLP) that focuses on detecting and classifying opinions expressed in textual data. In the digital social context, the increasing volume of public comments related to online gambling in Indonesia highlights the need to map public perception. This study aims to conduct an experimental analysis of the performance of three popular sentiment analysis approaches: VADER (Valence Aware Dictionary and sEntiment Reasoner), Naive Bayes, and Transformers-based models, specifically on Indonesian-language text. The dataset consists of public comments from social media and digital platforms containing keywords related to online gambling. The research process involves text preprocessing, data labeling, model training (for Naive Bayes and Transformers), and performance testing. Evaluation metrics include accuracy, precision, recall, and F1-score. The experimental results show that the Transformers model (using IndoBERT) achieves the highest performance in terms of accuracy and generalization ability, while VADER performs less optimally due to its limitations in understanding Indonesian linguistic context. Naive Bayes demonstrates moderate and consistent performance but lacks the capability to capture complex contextual meanings. These findings contribute to selecting appropriate sentiment analysis methods for non-English languages and support the development of more accurate public opinion detection systems in the future
Prediksi Harga Cryptocurrency Menggunakan Algoritma Temporal Fusion Transformer, N-Beats dan Deepar Nugraha Wahyu, Fajar; Anggai, Sajarwo; Tukiyat, Tukiyat
Ranah Research : Journal of Multidisciplinary Research and Development Vol. 8 No. 1 (2025): Ranah Research : Journal Of Multidisciplinary Research and Development
Publisher : Dinasti Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/rrj.v8i1.1949

Abstract

Cryptocurrency seperti Bitcoin, ETHereum, dan Solana memiliki volatilitas harga tinggi yang menyulitkan prediksi akurat. Penelitian ini bertujuan membandingkan akurasi tiga algoritma deep learning, yaitu Temporal Fusion Transformer (TFT), N-BEATS, dan DeepAR, dalam memprediksi harga harian ketiga aset tersebut. Data penelitian berupa harga penutupan, volume, dan kapitalisasi pasar yang diperoleh melalui CryptoDataDownload. Data diproses menggunakan normalisasi Min-Max Scaling, interpolasi linier untuk missing values, serta feature selection Pearson Correlation. Dataset kemudian dibagi ke dalam data pelatihan, validasi, dan pengujian dengan proporsi yang dapat disesuaikan, sehingga memungkinkan analisis pengaruh perbedaan pembagian data terhadap hasil model. Evaluasi dilakukan menggunakan MAE, RMSE, MAPE, dan R², serta uji statistik untuk menilai perbedaan signifikan antar model. Hasil penelitian menunjukkan bahwa N-BEATS memberikan performa terbaik dengan error paling rendah dan R² tertinggi, sementara TFT berada di urutan kedua dengan hasil yang cukup stabil. Sebaliknya, DeepAR secara konsisten memiliki performa terburuk dengan error tinggi dan R² negatif hampir di seluruh aset. Melalui eksperimen intensif, penelitian ini menunjukkan bahwa N-BEATS mengungguli TFT dan DeepAR dalam menjelaskan variansi data pada ketiga aset kripto: BTC, ETH, dan SOL. Pada semua dataset, N-BEATS mencapai nilai R² positif tertinggi di bawah Konfigurasi 2 (hidden size 32, 4 layers, dropout 0.3), dengan puncak 0.90 pada BTC, 0.93 pada ETH, dan 0.55 pada SOL. Nilai MAPE yang sesuai adalah 2.48% untuk BTC, 4.84% untuk ETH, dan 6.55% untuk SOL. Analisis juga mengungkap bahwa variasi ukuran hidden layer, epoch, dropout, jumlah layer, maupun pembagian data memengaruhi stabilitas serta performa prediksi, namun peningkatan kompleksitas tidak selalu menghasilkan performa yang lebih baik. Dengan demikian, N-BEATS dapat diidentifikasi sebagai model paling efektif untuk prediksi harga kripto, sekaligus memberikan kontribusi teoritis bagi pengembangan model peramalan deret waktu dan kontribusi praktis sebagai acuan bagi investor dalam pengambilan keputusan.
Analisis Stok Barang Menggunakan Algoritma K-Nearest Neighbor Dan Naïve Bayes Untuk Meningkatkan Efisiensi Persediaan Barang Retail Pada PT. XXX Restu Putra, Catur; Anggai, Sajarwo; Susanto, Agung Budi
Jurnal Ilmu Komputer Vol 4 No 1 (2026): Jurnal Ilmu Komputer (Edisi Januari 2026)
Publisher : Universitas Pamulang

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Abstract

Efficient inventory management is a strategic necessity for retail industries that operate under highly fluctuating demand conditions. PT XXX continues to experience inaccuracies in determining stock requirements because the analysis is still carried out manually using a three-month average sales calculation. This approach is unable to capture actual warehouse variations, resulting in frequent overstock and understock conditions. This study develops a machine learning–based stock classification model using the K-Nearest Neighbor (K-NN) and Naïve Bayes algorithms by utilizing key operational warehouse variables, including average sales, ending stock, and Days of Inventory (DOI). The dataset consists of 4,324 records from November 2024 to October 2025 and was processed using Orange Data Mining. Performance evaluation was conducted using accuracy, precision, recall, and confusion matrix. The results show that K-NN achieved the best performance, with 96.80% accuracy in the prediction model and 93.00% in the test & score evaluation, outperforming Naïve Bayes, which achieved approximately 90%. The study also produced a two-level classification mapping stock status (High/Low) and warehouse recommendations (Low/Enough/Excess) which revealed a significant imbalance between High and Low categories. These findings demonstrate that machine learning–based classification methods can enhance stock assessment accuracy and support more adaptive and efficient restocking decisions in retail inventory management
Analisis BERT dan LDA Untuk Ekstraksi Kebijakan Ekonomi Presiden Prabowo Subianto Muhammad Najwah; Sajarwo Anggai; Sudarno
Jurnal Ilmu Komputer Vol 4 No 1 (2026): Jurnal Ilmu Komputer (Edisi Januari 2026)
Publisher : Universitas Pamulang

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Economic policies introduced at the beginning of President Prabowo Subianto’s administration have generated diverse public discourses reflected in online media coverage. The large volume of textual data necessitates computational approaches to extract information systematically. This study aims to identify, label, and compare major economic policy topics using topic modeling techniques, namely Latent Dirichlet Allocation (LDA) and BERTopic.The dataset consists of 1,000 economic news articles collected through web scraping from an online news portal. Text preprocessing includes normalization, case folding, cleaning, tokenization, and lemmatization. LDA was implemented using a TF-IDF representation and evaluated with the Coherence Score (c_v). BERTopic employed IndoBERT embeddings, UMAP for dimensionality reduction, and HDBSCAN for hierarchical clustering, with evaluation based on topic coherence and semantic interpretability. The results show that LDA generated eight main topics with a Coherence Score (c_v) of 0.61, indicating moderate performance but limited semantic representation, leading to overlapping topics. In contrast, BERTopic produced nine main topics with a higher Coherence Score (c_v) of 0.72 and clearer, more contextual topic labels, including fiscal policy, energy, capital markets, and economic stimulus. Overall, BERTopic outperformed LDA in extracting and labeling economic policy topics due to its superior ability to capture semantic context and form stable topic clusters.
Analisis Topik Dan Sentimen Berbasis Algoritma Latent Dirichlet Allocation (LDA) Dan Bidirectional Encoder Representations From Transformers (BERT): Studi Kasus Ulasan Pelanggan Pada E-Commerce ruparupa.com Permana, Surya; Sajarwo Anggai; Taswanda Taryo
Jurnal Ilmu Komputer Vol 4 No 1 (2026): Jurnal Ilmu Komputer (Edisi Januari 2026)
Publisher : Universitas Pamulang

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The growth of e-commerce in Indonesia has led to an increasing volume of customer reviews containing vital information. These reviews are generally in the form of unstructured text, necessitating text analysis methods to extract meaningful insights. This study aims to analyze topics and sentiments in customer reviews of the e-commerce platform ruparupa.com by utilizing Latent Dirichlet Allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT) algorithms. The LDA algorithm is used to identify the main topics frequently discussed by customers, while BERT is employed to classify review sentiments into positive, negative, and neutral categories. By using Lexicon-Based and VADER as an automatic labeling mechanism (auto-labeling), the preprocessing stage includes cleaning, case folding, and stemming using the Sastrawi library to ensure the quality of the input data. The LDA algorithm is implemented to extract latent topic structures, which are then mapped into five main categories: Price, Application, Service, Product Quality, and Delivery. Furthermore, the DistilBERT model is trained through a fine-tuning process using the AdamW optimizer for 3 epochs. The sentiment analysis results indicate that the model demonstrates very strong performance, as reflected by high accuracy and consistently optimal precision, recall, and F1-score across all sentiment classes. This customer sentiment distribution reflects the level of user satisfaction with the services of ruparupa.com. The combination of LDA and BERT methods is proven effective in providing an overview of key issues and customer perceptions
SOSIALISASI ETIKA KECERDASAN ARTIFISIAL DAN PEMANFAATAN DALAM BIDANG PEMBELAJARAN DI ORGANISASI MASYARAKAT GENERASI REMAJA (GEMA) Anggai, Sajarwo; Musyafa, Ahmad; Toyib, Wildan
KOMMAS: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 1 (2026): KOMMAS: JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : KOMMAS: Jurnal Pengabdian Kepada Masyarakat

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Di era kemajuan teknologi, termasuk di bidang pendidikan, kecerdasan artifisial (AI) semakin penting. Namun, masih ada beberapa hal yang perlu ditangani sebagai tindak lanjut penerapan teknologi AI, terutama pemahaman dasar, etika, dan penerapan praktis. Organisasi GEMA belum pernah mengikuti kursus maupun sosialisasi mengenai etika kecerdasan buatan. Sebagai tindak lanjut, Tim PKM S2 Teknik Informatika Universitas Pamulang menyelenggarakan kegiatan yang berjudul "Sosialisasi Etika Kecerdasan Artifisial dan Pemanfaatannya dalam Bidang Pembelajaran" pada tanggal 19 Oktober 2025. Filosofi moral, cara menggunakan ChatGPT untuk menulis ilmiah, dan perkembangan kecerdasan buatan akan dibahas dalam kegiatan ini. Hasil evaluasi yang dilakukan terhadap peserta sosialisasi menunjukkan tingkat penerimaan yang dapat diterima secara positif, baik dari segi penyelenggaraan, materi, instruktur dan daya kreatif. Peserta diharapkan belajar menggunakan AI dengan bijak, kreatif, dan bertanggung jawab melalui pendekatan interaktif.
Pemanfaatan Artificial Intelligence dalam Pengembangan Aplikasi Pembelajaran di Era Digital Mahardika Paramarta Laia; Ismatullah; Mizanul Hakim; Muhammad Haikal Abdussalam; Yudi Candra; Harasta Rahman Tri Putra; Tukiyat; Sajarwo Anggai
APPA : Jurnal Pengabdian Kepada Masyarakat Vol 3 No 4 (2025): APPA : Jurnal Pengabdian kepada Masyarakat
Publisher : Shofanah Media Berkah

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Perkembangan teknologi kecerdasan buatan (Artificial Intelligence/AI) telah membawa transformasi signifikan dalam bidang pendidikan, khususnya dalam pengembangan aplikasi pembelajaran digital. Penelitian ini menganalisis pemanfaatan AI dalam meningkatkan efektivitas pembelajaran melalui personalisasi konten, analitik prediktif, dan umpan balik real-time, dengan validasi melalui studi kasus implementasi di SMA Muhammadiyah 08 Ciputat. Dengan menggunakan metodologi studi literatur dan evaluasi empiris, kami mengidentifikasi tiga tema utama: (1) peluang pedagogis melalui pembelajaran adaptif dan penilaian otomatis, (2) peningkatan engagement siswa hingga 60% melalui chatbot dan tutor virtual, dan (3) tantangan etika terkait privasi data dan bias algoritma. Hasil implementasi praktis menunjukkan bahwa penerapan aplikasi pembelajaran berbasis AI meningkatkan pemahaman siswa sebesar 40-50% pada kelompok pilot, meningkatkan motivasi belajar secara signifikan, dan membantu guru dalam memantau perkembangan belajar siswa secara sistematis. Evaluasi terhadap 20 siswa kelas X menunjukkan peningkatan keaktifan belajar, pemahaman konsep yang lebih baik, dan hasil posttest yang lebih tinggi dibandingkan dengan pembelajaran konvensional. Namun, diperlukan kerangka kerja etika yang kuat, pelatihan guru yang komprehensif, dan pendampingan berkelanjutan untuk memaksimalkan manfaat AI dalam pendidikan digital. Penelitian ini menyimpulkan bahwa AI memiliki potensi besar untuk menjadi katalis transformasi pendidikan di era digital, tetapi dengan tetap mempertahankan peran humanistik pendidik dan komitmen terhadap pembelajaran yang inklusif.
Co-Authors Abu Khalid Rivai Abu Khalid Rivai Agnia Bilqisti AGUNG BUDI SUSANTO Agung Budi Susanto Ali Imron Arya Adhyaksa Waskita Aslimah Brando, Charlo Chaerul Umam Cut Shifa Khoirunnisa Effendy, Rangga Febrian Hanto Sujatmiko Harasta Rahman Tri Putra Ismatullah Junedi, Beni Mahardika Paramarta Laia Masniari Samosir Maulana, Romdon Mizanul Hakim Muhamad, Faruk Muhammad Haikal Abdussalam Muhammad Najwah Muhammad Nana Muhammad Rafif Misbahuddin Murni Handayani musyafa, ahmad Nugraha Wahyu, Fajar Pahira, Wulan Permana, Surya Raden Gumilar Riyansyah Rafi Mahmud Zain Restu Putra, Catur Sabari Sabari Sabarrudin Slamet Wiyono Sudarno Sugiyo Suharyadi Supriatna, Dahlan Sutrisno, Sutrisno T., Thoyyibah Taswanda Taryo Taswanda Taryo Toyib, Wildan Tukiyat Tukiyat Tukiyat Tukiyat Tukiyat Tukiyat, Tukiyat Waskita, Arya Adhyaksa Wibowo, Rizki Septiyanto Wibowo, Satria Ardi Wily, Wily Arisandi Winarni Yudi Candra Zain, Rafi Mahmud