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All Journal 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 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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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.
Clustering Analysis of Stunting Risk Factors Using K-Means and Principal Component Analysis: A Case Study in Indonesian Regency Rohman, M. Hilma Minanur; Alzami, Farrikh; Hadi, Heru Pramono; Arifin, Zaenal; Sukamto, Titien Suhartini; Ashari, Ayu; Yusuf, Moh.
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.v9i1.14311

Abstract

Stunting, characterized by impaired growth and development in children, is one of the most serious public health problems often caused by chronic malnutrition. This study aims to identify patterns among stunting cases through clustering analysis of child health data. The algorithm used in this research uses K-Means. The dataset used in this study uses health data from 599 children in the Sambas Regency area of East Kalimantan Province. This dataset has several features that are quite diverse such as height, weight, age, nutritional intake, socioeconomic status, and others. This research process begins with cleaning the data, as well as looking at the correlation between features. One of the methods used is to conduct a data analysis process using Principal Component Analysis (PCA) which aims to reduce the dimensions of the data. After that, the process of finding the number of clusters using the Elbow method is carried out to determine the optimal number of clusters. This research uses 4 clusters in the process. The clustering results revealed that family structure (main family vs extended family) and parental income levels significantly influence stunting prevalence in the region.
Aspect-Based Sentiment Analysis for Enhanced Understanding of 'Kemenkeu' Tweets Sejati, Priska Trisna; Alzami, Farrikh; Marjuni, Aris; Indrayani, Heni; Puspitarini, Ika Dewi
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8558

Abstract

The perceptions and expressions shared by the public on social media play a crucial role in shaping the reputation of government institutions, such as the Ministry of Finance MOF (Kemenkeu) in Indonesia which also has faced increased scrutiny, particularly on Twitter. This study analyzes public sentiment towards the Indonesian Ministry of Finance (MoF) through Aspect-Based Sentiment Analysis (ABSA) on Twitter data. Using a dataset of 10,099 tweets from January to July 2024, this study combines IndoBERT for sentiment classification and Latent Dirichlet Allocation (LDA) for topic modeling. Here, LDA was tested across four scenarios that considered various combinations of stopwords removal and stemming techniques, resulting in coherence scores of 0.314256, 0.369636, 0.350285, and 0.541752. The most optimal results were achieved in the scenario of stopwords removal without stemming (with 0.314256 coherence score). The main results show: 1) Identification of four main topics related to MoF: Economy, Budget, Employees, and Tax; 2) The dominance of negative sentiment (6,837 tweets) compared to positive sentiment (198 tweets) across all topics; 3) The effectiveness of IndoBERT in handling the complexity of the Indonesian language, especially in interpreting context and language nuances; 4) The importance of proper preprocessing, with a scenario of removing stopwords without stemming resulting in the most relevant topics. This study provides valuable insights for MoF to understand public perception and identify areas that require special attention in public communication and policy.
Empowerment of Banyuanyar Village of Boyolali Regency through Utilization of Cow Manure as an Alternative Energy Source: Pemberdayaan Wilayah Desa Banyuanyar Kabupaten Boyolali melalui Pemanfaatan Kotoran Sapi menjadi Sumber Energi Alternatif Kusmiyati Kusmiyati*; Mahmud Mahmud; Farrikh Alzami; Sigit Muryanto; Risky Yuniar Rahmadieni
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 9 No. 1 (2025): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v9i1.22240

Abstract

Desa Banyuanyar merupakan salah satu desa di Kabupaten Boyolali dengan populasi sapi mencapai 1.840 ekor yang berpotensi menghasilkan limbah kotoran sebanyak 32 ton per hari. Limbah kotoran sapi yang dibuang ke lingkungan dapat menyebabkan masalah kesehatan dan lingkungan. Tujuan dari kegiatan pemberdayaan wilayah ini adalah untuk mengembangkan penggunaan biogas sebagai energi alternatif dengan memanfaatkan limbah kotoran sapi. Rangkaian kegiatan meliputi analisis masalah komunitas, pembangunan biodigester biogas, penggunaan biogas dan slurry, dan pelatihan penggunaan biodigester. Biodigester berhasil dibuat dengan produksi biogas 20 m3/batch dan mampu menggantikan penggunaan LPG untuk kompor. Slurry sebagai produk samping biogas digunakan untuk mensubstitusi sebagian penggunaan pupuk kimia. Polusi udara berupa bau tidak sedap dari kotoran sapi berkurang secara signifikan setelah adanya instalasi biogas. Hasil kegiatan pelatihan pada 69 peserta menunjukkan peningkatan pengetahuan dan kemampuan mengenai biodigester. Peningkatan skala produksi, peningkatan kualitas biogas, dan penambahan unit-unit pendukung dapat memaksimalkan manfaat dan keberlanjutan program ini
Clustering and Profiling Analysis for FIFA Football Player using K-Means Azzami, Salman Yuris Adila; Hadi, Heru Pramono; Alzami, Farrikh; Irawan, Candra; Nurhindarto, Aris; Sulistyono, MY Teguh
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 1 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i1.7897

Abstract

The selection of football players is a complex process involving talent evaluation based on various performance indicators, combining objective measures with subjective assessments by coaches and scouts. This research aims to improve the football player selection process using the K-Means clustering method based on the attributes of transfer price, performance, body specifications, position, and player ability. The dataset used consists of 17.947 players taken from the FIFA 19 edition of the soFIFA.com platform, which includes complete information such as transfer price, performance, body specifications, position, and player ability. The data was processed using principal component analysis (PCA) to reduce the dimensions, followed by the Elbow Method to determine the optimal number of clusters. The clustering results show the distribution of players based on their on-field roles, such as center back, goalkeeper, striker, and left wing back. The profiling of players from each cluster is identified based on position, body type, dominant foot usage, transfer price, and rating. This research provides useful insights for coaches and scouts in selecting players that suit specific roles in the team using better analysis. The findings also highlight the importance of player clustering for data-driven decision-making, which can optimize team composition and overall performance.
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.
LDA Topic Modeling: Twitter-Based Public Opinion on Indonesian Ministry of Finance Choirinnisa, Dina; Alzami, Farrikh; Indrayani, Heni; Rohmani, Asih; Nugraini, Siti Hadiati; Zulfiningrumi, Rahmawati; Susanti, Fitri
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.v9i2.14719

Abstract

People in the modern era use social media daily to exchange opinions regarding government policies, such as discussions related to the Indonesian Ministry of Finance (Kemenkeu). This study aims to analyze the topics of discussion about the Ministry of Finance on the Twitter platform, now known as 'X', and to determine the results of more effective preprocessing. The data in this study was taken from Twitter using the Tweet Harvest Tool with the keyword 'Ministry of Finance' from January 2024 to July 2024. The data is then processed through cleaning, preprocessing, calculation of coherence values, LDA modeling, and visualization. The preprocessing process includes several scenarios to compare the best results that are easy for the reader to understand. The highest coherence value obtained is 0.572250 by using stemming from NLTK library. The most effective preprocessing results are normalization, tokenization, stopwords, and stemming using Sastrawi. Modeling is done to find latent topics through LDA topic modeling techniques. Visualizing the intertopic distance map provides information on the distance between each topic. The results show that the distance between one topic and another has a variety of distance variations. This study shows that social media platforms can serve as a source of evaluation for the Indonesian government. The findings of these topics are helpful as insights for readers and the Kemenkeu. Finally, the analysis identified several key topics in public discussion, including fiscal policy, budget transparency, and the Ministry of Finance's performance in addressing current economic issues.  
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.
DESIGN OF IOT AND ONION AGRICULTURE DATABASE USING BPR LIFE CYCLE Thifaal, Nisrina Salwa; Alzami, Farrikh; Steven, Alvin; Yusianto, Rindra; Saputra, Filmada Ocky; Sartika, Mila; Andono, Pulung Nurtantio; 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 (632.824 KB) | DOI: 10.32832/moneter.v11i1.54

Abstract

One of the food commodities produced by the agricultural sector with high economic value is red onion. As the population of Indonesia increases, the need for red oniom has also increased. The level of red onion production from year to year is also increasing. Especially the central Java area as the largest red onion producing center in 2021. Therefore, the amount of red onion production needs to be maintained and increased by monitoring overall land conditions. Such as weather conditions, air, temperature, and humidity. A sensor to detect these factors is already available but there is no database to accommodate the data from the sensor. The purpose of this research is to produce a Business Process Model and Notation (BPMN) of red onion surveillance system on Internet of Things (IoT) based farmland. The stages carried out are by collecting data related to the research and analyzing business processes using the Business Process Reengineering Life Cycle (BPR) method. This method improves business processes to become more efficient and renewable. This research produces a database design to accommodate incoming data from Internet of Things sensors. Things (IoT) on red onion farming.
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 Arifin, Zaenal Aris Nurhindarto Ashari, Ayu Asih Rohmani, Asih 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 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 Pergiwati, Dewi Pratiwi, Yunita Ayu 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 Risky Yuniar Rahmadieni Ritzkal, Ritzkal 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 Sinaga, Daurat Soeleman, Arief Sri Handayani Sri Winarno Sri Winarno Steven, Alvin Subowo, Moh Hadi Sukamto, Titien Suhartini Sulistiyono, MY Teguh Sulistyowati, Tinuk Sutriawan Sutriawan Tamamy, Aries Jehan Thifaal, Nisrina Salwa Viry Puspaning Ramadhan Wellia Shinta Sari Wibowo, Isro' Rizky Widodo Yusianto Rindra Yuventius Tyas Catur Pramudi Zaenal Arifin Zahro, Azzula Cerliana Zulfiningrumi, Rahmawati