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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Transmisi: Jurnal Ilmiah Teknik Elektro Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Proceeding SENDI_U Jurnal Informatika Upgris JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Building of Informatics, Technology and Science Infotekmesin Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal Teknik Informatika (JUTIF) Moneter : Jurnal Keuangan dan Perbankan
Megantara, Rama Aria
Dian Nuswantoro University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mechanical Engineering

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Clustering IT Incidents Using K-Means: Improving Incident Response Time in Service Management Anggraeni, Rini; Alzami, Farrikh; Nurhindarto, Aris; Budi, Setyo; Megantara, Rama Aria; Rizqa, Ifan; Muslih, Muslih
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14822

Abstract

Incident management is one of the critical processes in Information Technology service management that aims to manage disruptions and minimize the impact of unexpected incidents on business services. This study applies the K-Means algorithm to cluster IT service incidents, aiming to enhance company operational efficiency. Utilizing a dataset from the UCI Machine Learning Repository comprising 141,712 events related to 24,918 incidents, this research analyzes incident patterns and characteristics for optimized handling. The data was analyzed through a series of preprocessing stages, and the elbow and silhouette methods were used to determine the optimal number of clusters. From the results, it was successfully grouped into 4 (four) clusters with a distortion score value of 964264294.569 and 0.52 silhouette score based on incident characteristics, such as urgency, priority, and number of reassignments. From this, the clustering results show that the K-Means algorithm effectively identifies incidents that require further handling, such as those with high urgency and priority, as well as helping the company focus resources to resolve incidents that have the most impact on the business sector. This research provides a data-driven solution to improve incident management and Service Level Agreement (SLA) fulfillment, while offering a framework for more effective and efficient IT incident analysis and resource allocation.
Implementation Of Extreme Gradient Boosting Algorithm For Predicting The Red Onion Prices Saputri, Pungky Nabella; Alzami, Farrikh; Saputra, Filmada Ocky; Andono, Pulung Nurtantio; Megantara, Rama Aria; Handoko, L Budi; Umam, Chaerul; Wahyudi, Firman
Moneter: Jurnal Keuangan dan Perbankan Vol. 11 No. 1 (2023): APRIL
Publisher : Universitas Ibn Khladun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (656.456 KB) | DOI: 10.32832/moneter.v11i1.55

Abstract

Red Onion or the Latin name Allium Cepa is included in the group of vegetable plants that are needed by the public for food needs. Red Onions are one of the seasonal crops so their availability can change in the market which causes price instability due to a lack of supply of production by several factors: 1) not yet it's harvest time, 2) crop attacked disease pests and fungi, and 3) weather factor. Therefore, a study is needed to predict red onion prices, so that it can be used as information for the government to stabilize red onion prices. The method used in this study is CRISP-DM and the Extreme Gradient Boosting algorithm to predict the price of red onions by taking data samples from Tegal and Pati Cities. The results of this study are that the Extreme Gradient Boosting algorithm is able to produce Tegal District Root Mean Square Error (RMSE) values of 5107.97% and Mean Absolute Percentage Error (MAPE) values of 0.17%. For prediction results with Pati Regency data samples, it produces a Root Mean Square Error (RMSE) value of 6049.74% and a Mean Absolute Percentage Error (MAPE) of 0.17%.
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.
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.
IMPLEMENTATION OF LSTM (LONG SHORT TERM MEMORY) ALGORITHM TO PREDICT WEATHER IN CENTRAL JAVA Irwan, Rhedy; Andono, Pulung Nurtantio; Al Zami, Farrikh; Ocky Saputra, Filmada; Megantara, Rama Aria; Handoko, L. Budi; Umam, Chaerul
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1118

Abstract

Agro-indutrial agricultural production such as red onions in Indonesia has a very important share in driving Indonesia's economic growth, especially in Central Java province which contributed 28.15% of the total national red onion production in 2021. Weather conditions have a major influence on the red onion planting process until the red onions are ready to be harvested. In this study, the objective is to predict various types of weather such as rainfall, air temperature, and air humidity in seven districts in Central Java, namely Brebes, Temanggung, Demak, Boyolali, Kendal, Pati, and Tegal. To do this, the use of the LSTM (Long Short Term Memory) algorithm with its ability to store memory longer than RNN will be reliable for predicting various types of weather in the future. This research was developed with the CRISP-DM (Cross Industry Process Model for Data Mining) method which has a goal-oriented approach, this method is a mature and widely accepted method in Data Mining with various applications in Machine Learning. With the final results from 39 models by using the evaluation of the average value of train MSE 0.013, test RMSE 0.11, test MSE of 0.02, test RMSE 0.12 and succeed to predict 5 days or months ahead from the last data that is provided.
PREDIKSI SENTIMEN MASYARAKAT TERHADAP PENGGUNAAN VAKSIN COVID 19 MENGGUNAKAN RNN prabowo, dwi puji; pramunendar, Ricardus anggi; Megantara, Rama Aria
Jurnal Informatika UPGRIS Vol 8, No 1: Juni 2022
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jiu.v8i1.11599

Abstract

Memahami sentimen dari opini publik terkait vaksin COVID-19 merupakan tantangan untuk meningkatkan penerimaan vaksin di masyarakat. Analisis sentimen telah memberikan banyak manfaat termasuk di bidang kesehatan. Analisis Sentimen dapat membantu memberikan gambaran yang dirasakan dan dipikirkan oleh para penerima vaksin. RNN merupakan salah satu metode deep learning yang sering diterapkan untuk penelitian analisis sentimen. RNN dengan arsitekur LSTM telah terbukti unggul dibandingkan metode deep learning lainnya dalam menyelesaikan tugas analisis sentimen. Penelitian ini mengusulkan model RNN-LSTM yang menerapkan arsitektur Bidirectional Layer (Bi-LSTM) agar penyerapan informasi kontekstual data lebih optimal karena data input diproses secara forward dan backward. Serta menambahkan mekanisme variational dropout pada layer LSTM untuk mendapatkan model yang optimal dan terhindar dari overfitting. Namun, keberhasilan dan keoptimalan model deep learning sangat bergantung pada ukuran dataset, jenis tugas dan penentuan parameternya. Dalam penelitian ini eksperimen terhadap nilai parameter arsitektur model dilakukan untuk mendapatkan model yang optimal dalam melakukan analisis sentimen opini publik terkait Vaksin COVID-19. Sehingga parameter terbaik didapatkan untuk model Bi-LSTM ini yaitu seperti berikut: maxlen =50, embedding size= 300, recurrent unit = 50, variational dropout = 0.25, optimizer Nadam, dan epoch = 100. Hasil evaluasi menunjukkan model BI-LSTM ini mampu melakukan analisis sentimen terhadap opini publik terkait vaksin COVID-19 ke dalam tiga kelas sentimen (positif, netral dan negatif) dengan baik dan mendapatkan akurasi sebesar 89.15% dengan rata-rata presisi 88%, recall 89% dan F1-score 88.43%
PENCAPAIAN KLASIFIKASI TERBAIK BERBASIS PERBAIKAN CITRA CLAHE DAN DARK CHANNEL PRIOR PADA SPESIES IKAN Pergiwati, Dewi; Anggi Pramunendar, Ricardus; Puji Prabowo, Dwi; Alzami, Farrikh; Megantara, Rama Aria
Jurnal Teknik Informatika UMUS Vol 7 No 2 (2025): November
Publisher : Universitas Muhadi Setiabudi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Ikan merupakan bahan pangan lauk-pauk utama yang dikonsumsi manusia untuk menunjang protein hewani dan zat-zat lain yang diperlukan tubuh. Ikan merupakan lauk-pauk pilihan utama yang memiliki harga relative murah dan mudah didapat. Namun pada nyatanya konsumsi ikan di Indonesia sangat rendah dibandingkan dengan negara-negara yang memiliki potensi sumberdaya perikanan yang jauh lebih rendah seperti negara Jepang, Korea Selatan, serta negara-negara di Asia lainnya. Di sisi lain, salah satu kekayaan Indonesia yang sangat berlimpah pada sector perairan adalah biota ikan. Dengan kondisi demikian, upaya peningkatan konsumsi ikan akan memberikan multiflier effect dalam lingkungan masyarakat. Selain meningkatkan tingkat kesehatan serta kecerdasan, juga semakin menggairahkan sektor perikanan untuk dapat mendorong peningkatan penyerapan tenaga kerja, meningkatkan pendapatan serta kesejahteraan pada masyarakat khususnya profesi nelayan, pembudidaya ikan, pengolah hasil ikan serta pihak terkait lainnya. Maka, perlu ditingkatkan kemampuan pengenalan ikan secara otomatis dengan bantuan computer untuk mengenali jenis-jenis ikan yang sangat beragam guna mempermudah proses pengelolaan dan distribusi ikan. Oleh karena itu dalam penelitian ini, peneliti ini mengusulkan untuk melakukan analisis dampak pre-processing dari kombinasi algoritma CLAHE dan DCP yang diterapkan dalam klasifikasi ikan dengan Random Forest.
Co-Authors Abu Salam Ahmad Akrom Ahmad Khotibul Umam, Ahmad Khotibul Akrom, Ahmad Al zami, Farrikh Al-Azies, Harun Alzami, Farrikh Anggi Pramunendar, Ricardus Ashari, Ayu Asih Rohmani, Asih Budi, Setyo Chaerul Umam Diana Aqmala Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Erika Devi Udayanti Fahmi Amiq Fauzi Adi Rafrastara Fikri Diva Sambasri Firman Wahyudi, Firman Guruh Fajar Shidik Hadi, Heru Pramono Harun Al Azies Herfiani, Kheisya Talitha Ifan Rizqa Ika Novita Dewi Irwan, Rhedy ISWAHYUDI ISWAHYUDI Khoirunnisa, Emila Kusumawati, Yupie L. Budi Handoko Lalang Erawan Lesmarna, Salsabila Putri Mahendra, Syafrie Naufal Moh. Yusuf, Moh. Muhammad Naufal, Muhammad Muslih Muslih Nazella, Desvita Dian Nurhindarto, Aris Ocky Saputra, Filmada Pergiwati, Dewi Puji Prabowo, Dwi Pulung Nurtantio Andono Puri Sulistiyawati Puri Sulistiyawati Ramadhan Rakhmat Sani Ricardus Anggi Pramunendar Rifqi Mulya Kiswanto Rini Anggraeni Ritzkal, Ritzkal Rofiani, Rofiani Rohman, Muhammad Syaifur Saputra, Filmada Ocky Saputra, Resha Mahardhika Saputri, Pungky Nabella Saraswati, Galuh Wilujeng Sasono Wibowo Sinaga, Daurat Soeleman, M Arief Sri Winarno Suharnawi Suharnawi Yuventius Tyas Catur Pramudi Zahro, Azzula Cerliana