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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Masyarakat Informatika Jurnal Simetris JAIS (Journal of Applied Intelligent System) Journal of Information System Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Dinamisia: Jurnal Pengabdian Kepada Masyarakat ILKOM Jurnal Ilmiah CARADDE: Jurnal Pengabdian Kepada Masyarakat IJISTECH (International Journal Of Information System & Technology) Building of Informatics, Technology and Science Infotekmesin International Journal of Engineering, Technology and Natural Sciences (IJETS) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknik Informatika (JUTIF) IJISTECH Moneter : Jurnal Keuangan dan Perbankan Jurnal Informatika: Jurnal Pengembangan IT
Alzami, Farrikh
Faculty Of Computer Science, Universitas Dian Nuswantoro
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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LDA Topic Analysis for Product Reviews in Social Media Platform Alzami, Farrikh; Megantara, Rama Aria; Prabowo, Dwi Puji; Sulistiyawati, Puri; Pramunendar, Ricardus Anggi; Dewi, Ika Novita; Ritzkal, Ritzkal
Moneter: Jurnal Keuangan dan Perbankan Vol. 11 No. 2 (2023): OKTOBER
Publisher : Universitas Ibn Khladun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/moneter.v11i2.402

Abstract

Social media in recent years is used as platform for product reviews and customer feedback. Thus, to understand the topic which have been discussed, we utilized Latent Dirichlet Allocation for topic modeling. The topic modeling is important due to it can gain insights into the specific features that customers like or dislike about a particular product. Thus, by not using stop words due it have possibilities remove the time domain, the information can be valuable for businesses as it helps them understand customer preferences and inform product development and marketing strategies with the coherence score 0.621520, the topic modeling obtained 3 optimal topics, where the topic 0 discussed about price and time it will be available to purchase. In topic 1 it discussed about the product is hard to obtain due to it not available in market. In topic 2, it discussed about ownership (what they like after usage).
RFM Analysis for Customer Lifetime Value with PARETO/NBD Model in Online Retail Dataset Megantara, Rama Aria; Alzami, Farrikh; Akrom, Ahmad; Pramunendar, Ricardus Anggi; Prabowo, Dwi Puji; Wibowo, Sasono; Ritzkal, Ritzkal
Moneter: Jurnal Keuangan dan Perbankan Vol. 11 No. 2 (2023): OKTOBER
Publisher : Universitas Ibn Khladun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/moneter.v11i2.409

Abstract

In recent years, there has been a growing interest in analyzing Customer Lifetime Value (CLV) due to its ability to provide valuable insights into customer profitability and worth. CLV analysis predicts the net profit attributed to the entire future relationship with a customer. This analysis involves calculating the present value of a customer's expected future spending with the company, facilitating an understanding of the economic value of long-term customer relationships. CLV analysis empowers businesses to identify their most profitable customers and develop strategies for retaining them, ultimately maximizing long-term profitability. CLV analysis relies on various models and techniques, including the RFM analysis categorizes customers based on recency, frequency, and monetary value, helping to segment customers and predict future behavior. Then, The Pareto/NBD model combines probability distributions to estimate CLV and is commonly used for customer base analysis. This research article explores the application of RFM analysis for estimating customer lifetime value using the Pareto/NBD model in an online retail dataset. This metric is crucial for businesses as it assists in identifying valuable customers and formulating retention strategies to maximize long-term profitability.
Fairer Public Complaint Classification on LaporGub: Integrating XLM-RoBERTa with Focal Loss for Imbalance Data Zahro, Azzula Cerliana; Alzami, Farrikh; Sani, Ramadhan Rakhmat; Fahmi, Amiq; Megantara, Rama Aria; Naufal, Muhammad; Azies, Harun Al; Iswahyudi, Iswahyudi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15260

Abstract

The advancement of digital technology has provided opportunities for governments to improve the quality of public services through citizen complaint channels. One example of this implementation in Indonesia is Lapor Gub, managed by the Dinas Komunikasi dan Informasi Provinsi Jawa Tengah (Communication and Information Agency of Central Java Province). This platform receives thousands of complaints daily, ranging from infrastructure, social issues, to illegal levies. However, the large volume of data and the imbalanced distribution of categories pose significant challenges for both manual and automated processing. This study aims to classify citizen complaint texts using XLM-RoBERTa combined with Focal Loss as an approach to handle data imbalance. The dataset consists of 53,774 complaints after data cleaning and text preprocessing. The training process applied a stratified split (78% training, 18% validation, 10% testing) and fine-tuning for 10 epochs. Model performance was evaluated using accuracy, precision, recall, and macro F1-score. The results show that the model without Focal Loss achieved 78.1% accuracy with a macro F1-score of 0.606, while the model with Focal Loss improved the macro F1-score to 0.625 with 78.5% accuracy. These findings demonstrate that the application of Focal Loss enhances the model’s ability to recognize minority categories without reducing performance on majority classes. Therefore, the combination of RoBERTa and Focal Loss offers an effective solution to support faster, fairer, and more transparent public complaint management.
Enhancing Entity Extraction in E-Government Complaint Data using LDA-Assisted NER Umam, Ahmad Khotibul; Alzami, Farrikh; Sani, Ramadhan Rakhmat; Rohmani, Asih; Prabowo, Dwi Puji; Pergiwati, Dewi; Megantara, Rama Aria; Iswahyudi, Iswahyudi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15292

Abstract

With the rapid development of information technology, governments are increasingly challenged to provide digital channels that enhance public participation in governance. LaporGub, an official platform managed by the Central Java Provincial Government, accommodates citizens' aspirations and complaints, but faces challenges in processing large amounts of unstructured text. Manual analysis is time-consuming and error-prone, resulting in delayed responses and decreased service quality. Conventional Named Entity Recognition (NER) models struggle to handle informal Indonesian-language text, while transformer-based approaches require substantial computing resources that are not widely available in local government environments. Therefore, this study aims to develop a lightweight NER approach by integrating Latent Dirichlet Allocation (LDA) as a semantic pre-annotation tool to improve the accuracy of entity extraction in Indonesian e-government complaint data. To achieve this goal, a dataset of 53,858 complaint reports from the LaporGub platform (2022–2025) was processed using LDA topic modeling (k=10) to provide semantic context during annotation. Next, the enriched dataset was used to train a spaCy-based NER model targeting three entity types: LOCATION, ORGANIZATION, and PERSON, with a training-validation-test split ratio of 70:15:15 using stratified sampling. The evaluation showed that the proposed NER+LDA model achieved a precision of 90.03%, a recall of 81.86%, and an F1-score of 85.75%, representing improvements of +5.78, +2.55, and +4.04, respectively, compared to the baseline NER model (F1-score: 81.71%). Furthermore, the most significant improvements occurred in the detection of ORGANIZATION and PERSON entities. These findings confirm that the integration of LDA as a pre-annotation strategy effectively improves NER performance on informal complaint texts in Indonesia, thus offering a practical and resource-efficient alternative to transformer-based methods for e-government applications.
Optimalisasi Arsitektur LSTM dengan Pendekatan Bidirectional untuk Deteksi Kantuk Pengemudi Berbasis Fitur Wajah Hartono, Andhika Rhaifahrizal; Naufal, Muhammad; Alzami, Farrikh
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.v7i2.8219

Abstract

Traffic accidents caused by driver fatigue and drowsiness remain a serious safety concern in many countries, including Indonesia. Various image-based drowsiness detection systems have been developed, yet many still rely on single-frame analysis and lack the ability to capture complete temporal context. To address this issue, a system capable of accurately and real-time detecting signs of drowsiness is required. This study aims to evaluate and compare the performance of Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) algorithms for a facial-feature-based drowsiness detection system. The dataset used is YawDD, which consists of videos of drivers yawning and in neutral conditions. Each video was decomposed into frames and analyzed using MediaPipe to extract facial landmarks. Two main features, Eye Aspect Ratio (EAR) and Mouth Opening Ratio (MOR), were utilized. Due to class imbalance, the SMOTE technique was applied to the minority class in the training data. Both LSTM and BiLSTM models were compared under similar architecture configurations. The results show that BiLSTM outperformed LSTM with an accuracy of 94,74% and an F1- score 94,82%, compared to 92,98% accuracy and 93,22% F1-score achieved by LSTM. These findings demonstrate that bidirectional sequential processing in BiLSTM is more effective in capturing the temporal patterns of drowsiness symptoms. This study contributes to the development of accurate and efficient computer vision-based drowsiness detection systems.
IbM Pemanfaatan Microsoft Word dalam Tata Kelola Pelaporan bagi Pengajar PAUD Korwilcam Jambu Alzami, Farrikh
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 2, No 2 (2019): Juli 2019
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (907.202 KB) | DOI: 10.33633/ja.v2i2.50

Abstract

 Dalam rangka peningkatan kualitas dan efisiensi pada pelaporan yang dilakukan oleh tenaga pengajar PAUD korwilcam Jambu, maka diperlukan pelatihan penggunaan Microsoft Word. Temuan yang ditemukan di lapangan mengindikasikan, bahwa pelaporan dan pengetikan dokumen diserahkan ke rental komputer terdekat sehingga berimbas pada lamanya pelaporan dan tidak terpakainya fasilitas komputer yang telah disediakan di PAUD masing-masing. Meskipun beberapa tenaga pengajar bisa menggunakan Microsoft Word, mereka hanya menggunakan sebagai pengganti mesin ketik saja tanpa tahu fitur lebih lanjut dari Microsoft Word tersebut. Dari hasil pelatihan, ditemukan bahwa Tenaga Pengajar PAUD dapat menggunakan dan terbantu pada banyak fitur yang ada pada Ms. Word seperti pembuatan surat menggunakan format dan mail merge, pembuatan dokumentasi laporan serta lintas platform dari Ms Word dan Excel dalam hal tabulasi grafis
Deep Learning Factor Investing in the Indonesian Stock Market Atha Rohmatullah, Fawwaz; Alzami, Farrikh; Rakhmat Sani, Ramadhan; Novita Dewi, Ika; Winarno, Sri; Sulistyono, Teguh
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.112549

Abstract

Traditional linear factor models often fail to capture the complex, non-linear dynamics of emerging stock markets. This research designs and validates a novel Recurrence Plot (RP) matrices with β-VAE deep learning methodology to discover non-linear investment factors within the Indonesian context. We demonstrate that this framework is a systematically superior "factor factory" compared to a linear RP with PCA baseline, discovering twice as many high-quality factors (Sharpe > 0.3) and generating 7-fold more alpha on average. A key finding is the model's ability to disentangle high-frequency predictive signals (identified by SHAP) from more valuable, low-frequency profitable trends (validated by backtesting). The champion factor from this process yields a robust annualized alpha of 6.65% with a minimal max drawdown of -7.73% from 2018 to 2025. This study concludes that the RP -> β -VAE approach is a robust and resilient framework for discovering safer, non-linear sources of return unexplained by conventional models.
DEEP LEARNING JARINGAN SARAF TIRUAN UNTUK PEMECAHAN MASALAH DETEKSI PENYAKIT DAUN APEL Sutriawan, Sutriawan; Fanani, Ahmad Zainul; Alzami, Farrikh; Basuki, Ruri Suko
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 11, No 1 (2023): Jurnal TIKomSiN, Vol. 11, No. 1, April 2023
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v11i1.729

Abstract

Diseases on apple leaves are becoming a major issue for apple growers since they can cause the crop to fail. Due to the diversity of diseases that can affect apple leaves, it can be challenging for farmers to determine the cause of leaf damage. The purpose of this research is to evaluate a convolutional neural network (CNN) method for its potential use in solving the problem of apple leaf disease identification. Four types of illness are dealt with: normal, multi-illness, rusty, and scabby. Many methods, such as data preparation and a preset VGG-16 artificial neural network (CNN) architecture, are recommended for use in the deep artificial neural network processing method. The most precise outcomes occurred when the beta parameter value was set to 2 = 0.999 at Ephoch to 85/100 with an accuracy of 0.7582, and when the epsilon parameter value was set to 1e-07 at Ephoch to 32/100 with an accuracy of 0.7582 with the best accuracy.
Improving Cervical Cancer Classification Using ADASYN and Random Forest with GridSearchCV Optimization Saputra, Resha Mahardhika; Alzami, Farrikh; Pramudi, Yuventius Tyas Catur; Erawan, Lalang; Megantara, Rama Aria; Pramunendar, Ricardus Anggi; Yusuf, Moh.
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2552

Abstract

Cervical cancer is a leading cause of death among women, with over 300,000 deaths recorded in 2020. This study aims to improve the accuracy of cervical cancer diagnosis classification through a combination of Adaptive Synthetic Sampling (ADASYN) and Random Forest algorithm. The research data was obtained from the Cervical Cancer dataset in the UCI Machine Learning Repository with an imbalanced data distribution of 95% negative class and 5% positive class. ADASYN method was chosen for its ability to handle imbalanced data by focusing on minority data points that are difficult to classify. The Random Forest algorithm was optimized using GridSearchCV to achieve maximum performance. Results show that this combination improved accuracy from 96.5% to 96.8% and recall from 93.7% to 94.3%. Feature importance analysis identified key risk factors such as number of pregnancies, age at first sexual intercourse, and hormonal contraceptive use that significantly influence diagnosis. This research demonstrates the effectiveness of combining ADASYN and Random Forest in enhancing classification performance for early cervical cancer detection.
Pengembangan Kemasan Produk untuk Meningkatkan Nilai Tambah Produk Kopi dan Susu Sapi Kusmiyati, Kusmiyati; Mahmud; Alzami, Farrikh; Muryanto, Sigit; Yuniar Rahmadieni, Risky
CARADDE: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 2 (2024): Desember
Publisher : Ilin Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31960/caradde.v7i2.2561

Abstract

Artikel ini membahas kegiatan pengabdian kepada masyarakat yang bertujuan untuk meningkatkan pengetahuan dan keterampilan para petani kopi dan susu sapi di Desa Banyuanyar, Kabupaten Boyolali, dalam desain dan pengembangan kemasan produk. Metode yang dilakukan dengan pelatihan yang didahului dengan survei awal untuk mengidentifikasi kebutuhan dan masalah yang dihadapi oleh para petani terkait dengan kemasan produk mereka. Survei ini melibatkan wawancara dan diskusi kelompok dengan para petani untuk mengumpulkan data yang relevan dan hasilnya dianalisis SWOT. Pelatihan ini berfokus pada konsep "Good Packaging" yang tidak hanya melindungi produk tetapi juga berfungsi sebagai media promosi yang efektif. Melalui pendekatan partisipatif, kegiatan ini diharapkan dapat meningkatkan nilai tambah produk kopi dan susu yang dihasilkan oleh petani lokal.
Co-Authors Abu Salam Aditya Rahman Adriani, Mira Riezky Ahmad Akrom Ahmad Akrom Ahmad Khotibul Umam, Ahmad Khotibul Ahmad Zainul Fanani Ahmad Zaniul Fanani Akrom, Ahmad Al-Azies, Harun Alpiana, Vika Alvin Steven Anggi Pramunendar, Ricardus Arifin, Zaenal Aris Nurhindarto Ashari, Ayu Asih Rohmani, Asih Atha Rohmatullah, Fawwaz Azzami, Salman Yuris Adila Budi, Setyo Candra Irawan Candra Irawan Caturkusuma, Resha Meiranadi Chaerul Umam Chaerul Umam Chaerul Umam Chaerul Umam Choirinnisa, Dina Dewi Agustini Santoso Diana Aqmala Dwi Puji Prabowo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Enrico Irawan Erika Devi Udayanti Esa Wahyu Andriansyah Fahmi Amiq Farah Syadza Mufidah Fikri Diva Sambasri Fikri Diva Sambasri Fikri Firdaus Tananto Fikri Firdaus Tananto Filmada Ocky Saputra Filmada Ocky Saputra Firman Wahyudi Firman Wahyudi Firman Wahyudi, Firman Fitri Susanti Ghina Anggun Hadi, Heru Pramono Hartono, Andhika Rhaifahrizal Harun Al Azies Hasan Aminda Syafrudin Herfiani, Kheisya Talitha Ifan Rizqa Ika Novita Dewi Ika Novita Dewi Indra Gamayanto Indra Gamayanto Indrayani, Heni ISWAHYUDI ISWAHYUDI Jumanto Karin, Tan Regina Khariroh, Shofiyatul Khoirunnisa, Emila Krisnawati, Dyah Ika Kukuh Biyantama Kukuh Biyantama Kusmiyati Kusmiyati Kusmiyati*, Kusmiyati Kusumawati, Yupie L. Budi Handoko Lalang Erawan Lesmarna, Salsabila Putri Mahmud Mahmud Marjuni, Aris Megantara, Rama Aria Mila Sartika Mila Sartika, Mila Mira Nabila Mira Nabila Moch Arief Soeleman Moh Hadi Subowo Moh. Yusuf, Moh. Muhammad Naufal, Muhammad Muhammad Noufal Baihaqi Muhammad Ridho Abdillah Muhammad Riza Noor Saputra Muhammad Rizal Nurcahyo Muslich Muslich, Muslich Muslih Muslih MY. Teguh Sulistyono Nuanza Purinsyira Nugraini, Siti Hadiati Nurhindarto, Aris Nurhindarto, Aris Nurwijayanti Pergiwati, Dewi Pratiwi, Yunita Ayu Puji Prabowo, Dwi Pulung Nurtantio Andono Pulung Nurtantyo Andono Puri Sulistiyawati Puri Sulistiyawati Puri Sulistiyawati Purwanto Purwanto Purwanto Purwanto Puspitarini, Ika Dewi Rama Aria Megantara Rama Aria Megantara Ramadhan Rakhmat Sani Ricardus Anggi Pramunendar Rifqi Mulya Kiswanto Rini Anggraeni Ritzkal, Ritzkal Rofiani, Rofiani Rohman, M. Hilma Minanur Ruri Suko Basuki Saputra, Filmada Ocky Saputra, Resha Mahardhika Saputri, Pungky Nabella Sasono Wibowo Sejati, Priska Trisna Sendi Novianto Sendi Novianto Sigit Muryanto, Sigit Sinaga, Daurat Soeleman, Arief Soeleman, M Arief Sri Handayani Sri Winarno Sri Winarno Steven, Alvin Subowo, Moh Hadi Sukamto, Titien Suhartini Sulistiyono, MY Teguh Sulistyono, Teguh Sulistyowati, Tinuk Sutriawan Sutriawan Tamamy, Aries Jehan Thifaal, Nisrina Salwa Viry Puspaning Ramadhan Wellia Shinta Sari Wibowo, Isro' Rizky Widodo Yuniar Rahmadieni, Risky Yusianto Rindra Yuventius Tyas Catur Pramudi Zaenal Arifin Zahro, Azzula Cerliana Zulfiningrumi, Rahmawati