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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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DEVELOPMENT OF TIME-SERIES-BASED MLOPS ARCHITECTURE FOR PREDICTING SALES QUANTITY IN MICRO, SMALL, AND MEDIUM ENTERPRISES (MSMES) Lesmarna, Salsabila Putri; Alzami, Farrikh; Rizqa, Ifan; Salam, Abu; Aqmala, Diana; Megantara, Rama Aria; Pramunendar, Ricardus Anggi
Transmisi: Jurnal Ilmiah Teknik Elektro Vol 26, No 2 April (2024): TRANSMISI: Jurnal Ilmiah Teknik Elektro
Publisher : Departemen Teknik Elektro, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/transmisi.26.2.64-69

Abstract

Micro, Small, and Medium Enterprises (MSMEs) constitute a significant portion of the economy in many developing countries, playing a vital role in employment generation and economic growth. Sales performance is a critical factor for MSMEs, influenced by various internal and external factors. Time-series analysis offers a valuable tool to predict sales quantities by analyzing historical data and identifying patterns and trends. In this context, the SARIMAX (Seasonal Autoregressive Integrated Moving Average with Exogenous Variables) model emerges as a suitable method to forecast future sales, leveraging both historical data and external variables. This research explores the synergy between time-series analysis, specifically SARIMAX modeling, and MLOps (Machine Learning Operations). Finally, this research aims to provide a framework for the practical application of MLOps to enhance sales forecasting and decision-making processes within MSMEs, fostering their growth and sustainability in a competitive market landscape.
Analisis Topic-Modelling Menggunakan Latent Dirichlet Allocation (LDA) Pada Ulasan Sosial Media Youtube Alpiana, Vika; Salam, Abu; Alzami, Farrikh; Rizqa, Ifan; Aqmala, Diana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7127

Abstract

This research explores the role of Micro, Small, and Medium Enterprises (MSMEs) in the Indonesian economy, focusing on sales and marketing challenges in the era of social media, especially YouTube. With millions of individuals using this platform to share product insights, reviews, and experiences, MSMEs need to receive relevant feedback. This study applies text mining, particularly the topic modeling analysis method with Latent Dirichlet Allocation (LDA), to analyze user comments on MSME videos, with an emphasis on Lumpia Gang Lombok Semarang on YouTube. Through the application of LDA, the identification of ten main topics is conducted, with the highest coherence value reaching 0.414027. The visualization of the intertopic distance map provides an understanding of the relationships between topics and dominant words. Comment analysis provides valuable insights into user preferences and perceptions of products, supporting MSMEs in understanding customer satisfaction and enhancing value for those enterprises. These findings also affirm the effectiveness of YouTube as a relevant data source for understanding public preferences for MSME products. This research details text processing methods, including extraction, cleaning, tokenization, normalization, removal of stopwords, and stemming. With this approach, the research not only provides insights into topic analysis in the context of social media but also makes a valuable contribution to the development and marketing of MSMEs through a better understanding of social media data, especially on the YouTube platform.
Analisis Perbandingan Algoritma Naive Bayes Classifier dan Support Vector Machine untuk Klasifikasi Berita Hoax pada Berita Online Indonesia Sani, Ramadhan Rakhmat; Pratiwi, Yunita Ayu; Winarno, Sri; Udayanti, Erika Devi; Alzami, Farrikh
Jurnal Masyarakat Informatika Vol 13, No 2 (2022): JURNAL MASYARAKAT INFORMATIKA
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.13.2.47983

Abstract

Masyarakat mampu mengkonsumsi tiap informasi yang tersebar di internet dengan cepat dan terkadang informasi yang beredar tidak selalu memberikan kebenaran yang sesuai dengan kenyataannya (hoax). Demi mendapatkan keuntungan dan mencapai tujuan pribadi, hoax seringkali sengaja dibuat dan dibagikan. Informasi yang didapatkan dari hoax tentunya dapat mempengaruhi masyarakat karena menimbulkan keraguan dan kebingungan terhadap informasi yang diterima Oleh karena itu, penelitian ini membahas tentang bagaimana mengklasifikasikan berita hoax berbahasa Indonesia mengenai isu kesehatan menggunakan TF-IDF serta algoritma Naïve Bayes Classifier dan Support Vector Machine dengan 4 model yang berbeda sehingga mampu memprediksi sebuah berita hoax atau valid. Pada penelitian ini dataset yang dikumpulkan sebanyak 287 diantaranya 200 valid dan 87 hoax. Hasil evaluasi model penelitian ini dengan menggunakan 4 model berbeda pada masing-masing algoritma, diperoleh nilai classification report terbesar untuk algoritma NBC pada model Complement Naïve Bayes dengan hasil precision 95.4%, recall 95.4%, f1-score 95.4% dan accuracy 93.1%. Sedangkan nilai classification report terbesar untuk algoritma SVM pada kernel Sigmoid dengan hasil precision 95.6%, recall 100%, f1-score 97.7% dan accuracy 96.5%. Sehingga dapat disimpulkan bahwa hasil performa rata-rata dari algoritma SVM memiliki kinerja yang lebih baik jika dibandingkan dengan algoritma NBC dalam melakukan klasifikasi berita hoax mengenai isu kesehatan.
Peningkatan Deteksi Posisi Wajah Manusia dengan Metode Normal PDF berbasis Algoritma Viola-Jones Pramunendar, Ricardus Anggi; Megantara, Rama Aria; Alzami, Farrikh; Prabowo, Dwi Puji; Pergiwati, Dewi; Sinaga, Daurat
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 15, No 1 (2024): JURNAL SIMETRIS VOLUME 15 NO 1 TAHUN 2024
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v15i1.10617

Abstract

Deteksi kulit manusia dalam pengolahan citra memiliki peran penting dalam aplikasi seperti analisis gerakan, pencarian citra berbasis konten, interaksi manusia komputer, dan analisis pelacakan gerakan manusia. Meskipun banyak penelitian telah dilakukan, masih ada kendala dalam menghadapi variasi warna kulit manusia yang kompleks. Dalam penelitian ini, diusulkan peningkatan kinerja deteksi kulit manusia dengan memanfaatkan algoritma deteksi wajah Viola-Jones untuk menentukan posisi wajah dalam citra. Selain itu, diterapkan juga teknik pemisahan region kasar dan halus pada wajah guna meningkatkan hasil deteksi kulit manusia. Penggunaan Normal PDF digunakan untuk mencari probabilitas piksel kulit dalam citra. Metode yang diusulkan berhasil mencapai tingkat akurasi tinggi, di mana sebagian besar citra uji memiliki akurasi di atas 90%. Meskipun terdapat beberapa citra yang memiliki akurasi lebih rendah dibandingkan metode sebelumnya, secara keseluruhan metode yang diusulkan mampu meningkatkan kinerja deteksi kulit manusia. Oleh karena itu, penelitian ini memberikan kontribusi berharga dalam pengembangan metode deteksi kulit manusia yang lebih baik.
Deep Convolution Neural Network to solve Problems for Appel Leaf Disease Detection Sutriawan; Ahmad Zaniul Fanani; Farrikh Alzami; Ruri Suko Basuki
Jurnal Internasional Teknik, Teknologi dan Ilmu Pengetahuan Alam Vol 5 No 2 (2023): International Journal of Engineering, Technology and Natural Sciences
Publisher : Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46923/ijets.v5i2.232

Abstract

Orchardists are now concerned about apple leaf infections since they could result in crop failure. It is challenging for growers to identify the type of illness on apple leaves due to the large variety of diseases that can affect apple leaves. In this study, we cover four different illness categories: healthy, numerous diseased, rusty, and scabby. a deep convolutional neural network method of processing. using a number of suggested methods, including data preprocessing and the pre-configured VGG-16 deep convolutional neural network (CNN) architecture. The Adam optimization model's beta 2 = 0.999 parameter value at Ephoch to 85/100 with an accuracy of 0.7582 and epsilon = 1e-07 parameter value at Ephoch to 32/100 with an accuracy of 0.7582 both produced the best accuracy outcomes.
Using 2024 election twitter data, sentiment analysis based on TF-IDF and Naïve Bayes Moh Hadi Subowo; Alzami, Farrikh
Moneter: Jurnal Keuangan dan Perbankan Vol. 12 No. 2 (2024): JULI
Publisher : Universitas Ibn Khladun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/moneter.v12i2.789

Abstract

This research explores sentiment analysis within the context of the 2024 Presidential Election, utilizing tweet data to understand public opinion dynamics. The study employs the Naive Bayes classification algorithm combined with Term Frequency-Inverse Document Frequency (TF-IDF) techniques to categorize sentiments into diverse emotional spectrums. During the training phase, the model achieved a high accuracy of 98.3%. However, when applied to real-world data, the accuracy dropped to 80.8%, highlighting the challenges of adapting to unpredictable and heterogeneous data. The evaluation showed the model's effectiveness in recognizing positive sentiments in training data, but a decrease in performance during testing. This underscores the need for dynamic training approaches to handle real-world applications. The study demonstrates that TF-IDF significantly enhances the Naive Bayes classifier's accuracy and that tweet data on the 2024 Presidential Election predominantly exhibit positive sentiments. However, these findings require cautious interpretation due to the complexities of natural language and potential cultural biases in automated sentiment analysis. The research suggests addressing overfitting, diversifying the training corpus, and adopting more sophisticated algorithms to better capture nuanced sentiments. This study lays the groundwork for future research in understanding public opinion during election cycles.
Optimizing Parcel Package Selection Using an Enhanced Multiple Knapsack Problem Approach with Greedy Dynamic Programming Santoso, Dewi Agustini; Rizqa, Ifan; Aqmala, Diana; Alzami, Farrikh
Moneter: Jurnal Keuangan dan Perbankan Vol. 12 No. 3 (2024): OKTOBER
Publisher : Universitas Ibn Khladun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/moneter.v12i3.996

Abstract

This study introduces an enhanced algorithm for solving the Multiple Knapsack Problem to optimize parcel package selection, particularly focusing on balancing value maximization with price and weight constraints. Due to Dynamic Programming requires significant memory, The proposed method employs a heuristic approach such as greedy algorithm, initially sorting items by their value-to-weight ratio and iteratively filling each knapsack to maximize total value while adhering to constraints. The algorithm demonstrates high computational efficiency, solving instances within 0.07 seconds, and achieves a total value of 122.00 across multiple knapsacks. However, the analysis reveals a significant underutilization of weight capacity (44.74%) compared to price capacity (98.92%), highlighting the need for more sophisticated constraint handling. Limitations such as the simplistic heuristic approach and single-objective focus are discussed. Future work will explore the integration of advanced optimization techniques, dynamic constraints, and multi-objective frameworks to improve solution quality and applicability in real-world scenarios. This research contributes to the field by providing a foundation for further exploration of Multiple Knapsack Problem in logistics and resource allocation contexts.
Enhanced Semarang Batik Classification using MobileNetV2 and Data Augmentation Khoirunnisa, Emila; Alzami, Farrikh; Pramunendar, Ricardus Anggi; Megantara, Rama Aria; Naufal, Muhammad; Al-Azies, Harun; Winarno, Sri
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14308

Abstract

Batik, an Indonesian cultural heritage recognized by UNESCO, faces challenges in pattern identification and documentation, particularly for the younger generation. Previous studies on batik classification have shown limitations in handling small datasets and maintaining accuracy with limited computational resources. This research proposes an enhanced classification approach for Semarang Batik motifs using MobileNetV2 architecture combined with strategic data augmentation techniques. The study utilizes a dataset of 3,020 images comprising 10 distinct Semarang Batik motifs, implementing horizontal flipping, rotation, and zoom transformations to address dataset limitations. Our methodology incorporates transfer learning through ImageNet pre-trained weights and custom layer modifications to optimize the MobileNetV2 architecture for batik-specific features. The model achieves 100% accuracy on validation data, with precision, recall, and F1-scores consistently above 0.98 across all classes. The confusion matrix analysis reveals minimal misclassification between similar motif patterns, particularly in the Batik Blekok Warak and Batik Kembang Sepatu classes. This research contributes to cultural heritage preservation by providing an efficient, resource-conscious solution for automated batik pattern recognition, potentially supporting educational and commercial applications in the batik industry.
Implementation of LSA for Topic Modeling on Tweets with the Keyword ‘Kemenkeu’ Khariroh, Shofiyatul; Alzami, Farrikh; Indrayani, Heni; Dewi, Ika Novita; Marjuni, Aris; Adriani, Mira Riezky; Subowo, Moh Hadi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14309

Abstract

This research explores public discourse on financial policies by analyzing tweets mentioning the keyword 'Kemenkeu' (Ministry of Finance). Using Latent Semantic Analysis (LSA), the study examined 10,099 tweets to uncover key topics that reflect public sentiment toward the Ministry’s policies. Preprocessing steps, such as stopword removal and stemming with Sastrawi, were essential to ensure the effectiveness of the analysis. The results revealed three main topics: Finance and Budget, Salaries and Employee Welfare, and Excise and Customs Regulations. These insights provide a better understanding of public opinion on financial issues and highlight the importance of proper text preprocessing in topic modeling. This approach demonstrates how LSA can be used as a tool for analyzing large-scale social media data, offering valuable input for policymakers. Future research could expand on this by using more advanced models or larger datasets to gain deeper insights.
Predicting IT Incident Duration using Machine Learning: A Case Study in IT Service Management Caturkusuma, Resha Meiranadi; Alzami, Farrikh; Nurhindarto, Aris; Sulistiyono, MY Teguh; Irawan, Candra; Kusumawati, Yupie
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14310

Abstract

In the digital era, ensuring customer satisfaction with IT services is crucial for business success. However, the complexity of IT infrastructure makes it difficult to manage services, requiring companies to focus on improving efficiency and reducing operational costs. One of the strategies used is Information Technology Service Management (ITSM), the main component of which is incident management, which aims to minimize service disruptions. While various studies on ITSM exist, research focused on Machine Learning models for predicting incident resolution times is relatively limited. This research aims to develop an incident resolution duration prediction model using a Random Forest Regressor-based regression approach. The dataset used is an event log from the ServiceNow system containing data on 24,918 incidents. The model was evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R2 metrics, where the model achieved a MAE of 14.33 hours, RMSE of 69.8 hours, and R2 of 0.98. These results show that the model can provide accurate predictions and support better decision-making in IT incident handling. Time-related features, such as sys_update_month and closed_month, proved to be the most influential factors in predicting incident resolution duration.
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