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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Edutech Journal of Information System Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Pilar Nusa Mandiri JOURNAL OF APPLIED INFORMATICS AND COMPUTING Budimas : Jurnal Pengabdian Masyarakat Abdimasku : Jurnal Pengabdian Masyarakat Devotion: Journal of Research and Community Service Prosiding Seminar Nasional Rekayasa dan Teknologi (TAU SNAR- TEK)
Asih Rohmani, Asih
Program Studi Sistem Informasi, Fakutas Ilmu Komputer, Universitas Dian Nuswantoro
Religion Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences Other

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Strategic Clustering of Poverty Areas in Central Java Using K-Means and Silhouette Evaluation Tacharri, Chusnuut; Rohmani, Asih; Fahmi, Amiq
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.v9i2.14734

Abstract

Indonesia is one of several developing nations that struggle with poverty. Central Java is one of Indonesia's provinces with the third-highest percentage of the country's inadequate. This study aims to explore and improve the application of the K-Means Algorithm in investigating socioeconomic disparities. In this study, the Elbow method is used to determine the optimal number of clusters to overcome the weaknesses in determining the number of clusters in conventional K-Means. Model evaluation using the silhouette coefficient shows the effectiveness of this method approach with a value of 0.504 and several clusters (K = 3), which meets the medium structure category. The Human Development Index (HDI) and Uninhabitable Households (RTLH) were two criteria used to categorize poverty areas using the K-Means Algorithm optimization successfully. According to the clustering results, there were 12 regions in Cluster 0, 2 in Cluster 1, and 21 in Cluster 2. These findings are anticipated to offer the Central Java Provincial Government critical insights, facilitating the development of precise and well-targeted initiatives to address deprivation issues effectively. Furthermore, a more systematic and structured optimization of the K-Means algorithm has the potential to significantly improve both the accuracy and practical relevance of studies on socioeconomic inequality in Central Java Province. This enhanced methodological approach can provide more in-depth results on data-driven regional disparities to reduce these disparities comprehensively.
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.
Development of Fishery Marketplace Applications Using Prototype Methods Bao Sumantoro, Yeremia Joen; Rohmani, Asih; Budiman, Fikri; Sugiarto, Edi
Devotion : Journal of Research and Community Service Vol. 4 No. 2 (2023): Devotion: Journal of Research and Community Service
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/devotion.v4i2.408

Abstract

Fish farming is an activity of raising fish or developing fish in a controlled environment. However, if you look at the statistical data, the fish consumption level of Kendal Regency residents is still low, namely 24 kg/cap/year, far below the national fish consumption rate. From the existing problems, fish cultivators, especially those in the border areas of Kendal Regency, mostly sell their fish production outside the area. As a result, the selling price is lower because it is deducted from the transport price. Another problem occurs for cultivators who are just starting a business. Usually they have sufficient capital to build a business, but they do not yet have acquaintances with fish traders who are willing to buy their crops. So they often use social media facilities to offer their crops. Based on the existing problems, it is possible to help solve problems by utilizing information technology. Utilization of a technology in selling fish can help farmers so that sales are more focused and produce good sales, so an application is made that is expected to help overcome existing problems, researchers use the prototype method to find out user needs
Implementing BOOM in Designing a Knowledge Management System for the Information Systems Study Program at Dian Nuswantoro University Sani, Ramadhan Rakhmat; Sukamto, Titien S.; Rohmani, Asih; Maszuda, Akbar Alvian
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.3677

Abstract

Knowledge management within the Information Systems Study Program at Dian Nuswantoro University is currently still at an ineffective stage. Although the program focuses on the development and distribution of information systems, the lack of an implemented Knowledge Management System (KMS) has hindered the effective organization and utilization of knowledge within the department. By implementing a KMS, the program could more easily manage, store, and collaborate on knowledge assets. To address this issue, a web-based KMS design is proposed using the Business Object Oriented Modelling (BOOM) method. This method involves several stages: SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis, discovery, construction, and final verification and validation—each aimed at optimizing knowledge management within the academic scope of the study program. The design process was supported by data collected through questionnaires distributed to both faculty members and students. The final output of this study includes a test scenario and user interface design for the Information Systems Program's Knowledge Management System website. This proposed design is expected to enhance and streamline knowledge management within the department.
PELATIHAN PEMBUATAN ECO ENZYM DAN PRODUK TURUNANNYA SEBAGAI STRATEGI UNTUK MENGURANGI TIMBULAN SAMPAH ORGANIK DAN MENINGKATKAN PENDAPATAN MASYARAKAT Kinanty, Septyana Retno; Pangestu, Estri Pramudia; Rohmani, Asih; Sejati, Priska Trisna; Ariyanto, Muhammad; Wijayanti, Selvi; Arifin, Muhammad Farhan
BUDIMAS : JURNAL PENGABDIAN MASYARAKAT Vol. 6 No. 3 (2024): BUDIMAS : Jurnal Pengabdian Masyarakat
Publisher : LPPM ITB AAS Indonesia Surakarta

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

Abstract

Abstrak Kota Semarang yang merupakan ibukota Provinsi Jawa Tengah memiliki jumlah penduduk kurang lebih 1,6 juta orang yang tersebar di 16 kecamatan dan 117 kelurahan. Namun, di siang hari, jumlah penduduk meningkat hingga 2,5 juta orang yang mengakibatkan besarnya jumlah sampah yang dihasilkan, yaitu mencapai 1.000 ton per hari. Komposisi sampah berdasarkan jenis sampah yang dihasilkan pada tahun 2023 menunjukkan bahwa jenis sampah terbanyak adalah sisa makanan, yaitu mencapai 40,8%. Hal ini menimbulkan masalah bagi masyarakat Tugurejo, antara lain adanya pencemaran udara karena bau tidak sedap, munculnya hewan-hewan pemakan sampah yang bisa menyebarkan penyakit dan potensi terjadinya banjir. Sampah organik bisa diolah menjadi eco enzyme, yaitu fermentasi limbah dapur organik seperti ampas buah dan sayuran, gula (gula coklat, gula merah atau gula tebu), dan air. Eco Enzymbisa dimanfaatkan sebagai bahan dasar pembuatan sabun cuci, baik cuci piring, cuci pakaian bahkan sabun mandi. Manfaat yang besar dari Eco Enzymini ditularkan kepada masyarakat Tugurejo, sehingga mereka diberi pelatihan bagaimana cara membuat Eco Enzymdan produk turunannya yang berupa sabun cuci cair. Hasil dari program pengabdian ini adalah meningkatnya pengetahuan masyarakat akan pengelolaan sampah organik dan potensi untuk meningkatkan pendapatan melalui produk turunan eco enzyme. Kata Kunci : Eco Enzyme, sampah organik, pilah sampah Abstract Semarang City, the capital of Central Java Province, has a population of approximately 1.6 million people spread across 16 sub-districts and 117 villages. However, during the day, the population increases to 2.5 million people, resulting in a large amount of waste produced, reaching 1,000 tons per day. The composition of waste based on the type of waste produced in 2023 shows that the largest type of waste is food waste, reaching 40.8%. This causes problems for the Tugurejo community, including air pollution due to unpleasant odors, the emergence of garbage-eating animals that can spread disease and the potential for flooding. Organic waste can be processed into eco enzymes, which are fermented organic kitchen waste such as fruit and vegetable dregs, sugar (brown sugar, brown sugar or cane sugar), and water. Eco enzymes can be used as a basic ingredient for making laundry soap, both for washing dishes, washing clothes and even bath soap. The great benefits of eco enzymes are passed on to the Tugurejo community, so that they are given training on how to make eco enzymes and their derivative products in the form of liquid laundry soap. The results of this community service program are increased public knowledge of organic waste management and the potential to increase income through Eco Enzymderivative products. Key Word : Eco Enzyme, Organic waste, sort the trash
Optimization of Early Diagnosis Prediction Models for Acute Respiratory Infections (ARI) in Children Using Decision Tree, Random Forest, and Resampling Techniques Firdaus, Caesario Gumilang; Rohmani, Asih; Suharnawi, Suharnawi
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11558

Abstract

Acute Respiratory Tract Infections (ARI) are the leading cause of childhood morbidity in Indonesia, with challenges in early detection due to limited medical personnel and diagnostic data imbalance, where LRTI cases are far fewer than URTI cases. This study developed and optimized an ARI classification prediction model (URTI and LRTI) based on machine learning with resampling techniques to address imbalance. An explanatory quantitative design was used with secondary data from the Mijen Community Health Center, Semarang (2020–2025, 12.177 valid data), with preprocessing including outlier handling (Winsorizing, IQR), stratified split (70:30), and RobustScaler on the training data. Three resampling techniques (SMOTE, ADASYN, SMOTE-ENN) were applied, then tested using Decision Tree and Random Forest with GridSearchCV and 5-fold cross-validation, focusing on Recall and AUC-PR evaluation for minority classes. The results showed that Random Forest with SMOTE-ENN provided the best performance, increasing the LRTI recall from 0.02 to 0.37 and F1-macro to 0.54, while Decision Tree with SMOTE-ENN produced the highest AUC-PR of 0.31. Despite this significant improvement, a recall of 0.37 is still low for clinical applications because the risk of false negatives remains high, potentially delaying patient treatment Future implementation requires the integration of clinical symptom data (e.g., respiratory rate) to achieve clinically acceptable sensitivity. These findings confirm that resampling can improve model capabilities, but additional feature exploration is needed to achieve adequate diagnostic sensitivity in the context of healthcare analytics.
Analisis Kausalitas Banjir Berulang di Kabupaten Grobogan: Pendekatan Kecerdasan Buatan yang dapat diinterpretasi untuk Mitigasi Berbasis Bukti Kuswardono, Danang; Tanuji, Hadi; Prabowo, Dwi Puji; Rohmani, Asih
JOINS (Journal of Information System) Vol. 10 No. 2 (2025): Edisi November 2025 (ongoing)
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v10i2.14346

Abstract

Banjir berulang di Kabupaten Grobogan, Jawa Tengah, menimbulkan kerugian signifikan dan mengancam keberlanjutan wilayah. Pendekatan konvensional seringkali terbatas dalam mengidentifikasi pola kompleks dan hubungan kausalitas antar faktor pemicu banjir. Penelitian ini mengusulkan kerangka kerja analisis kausalitas banjir menggunakan Kecerdasan Buatan (AI) yang dapat diinterpretasi (Explainable AI/XAI) untuk mengungkap faktor-faktor dominan (hidrologis, geografis, geologis, dan antropogenik) yang berkontribusi terhadap fenomena ini. Dengan memanfaatkan data spasial-temporal yang komprehensif dan metode AI seperti SHAP dan Grad-CAM, penelitian ini bertujuan untuk mengukur kontribusi masing-masing faktor pemicu, memberikan pemahaman yang lebih mendalam tentang mekanisme banjir. Hasil yang diharapkan akan mendukung perumusan strategi mitigasi yang lebih tepat sasaran dan berbasis bukti, beralih dari respons reaktif menjadi pendekatan proaktif dalam pengelolaan risiko bencana di Kabupaten Grobogan. Hasil yang diharapkan menunjukkan bahwa metode XAI mampu menampilkan kontribusi relatif setiap faktor pemicu banjir, sehingga interpretasi model menjadi lebih transparan dibandingkan pendekatan tradisional. Kerangka kerja ini diproyeksikan dapat meningkatkan akurasi analisis sekaligus mempercepat proses identifikasi wilayah prioritas untuk mitigasi
Co-Authors Abdul Syukur Ahmad Khotibul Umam, Ahmad Khotibul Alzami, Farrikh Arifin, Muhammad Farhan ARIYANTO, MUHAMMAD Arta Moro Sundjaja, Arta Moro Ayuningsih, Dewi Putri Bao Sumantoro, Yeremia Joen Choirinnisa, Dina Danang Kuswardono Dwi Puji Prabowo, Dwi Puji Edi Sugiarto Fahmi Amiq Fikri Budiman Firdaus, Caesario Gumilang Fitri Susanti Hadi Tanuji Ibnu Utomo WM Ika Novita Dewi Indra Gamayanto Indrayani, Heni ISWAHYUDI ISWAHYUDI Karin, Tan Regina Kinanty, Septyana Retno Kurnia Desita, Raafi Maszuda, Akbar Alvian Megantara, Rama Aria Muslih Muslih MY. Teguh Sulistyono Nugraha, Rizka Nugraini, Siti Hadiati Pangestu, Estri Pramudia Pergiwati, Dewi Pulung Nurtantio Andono Ramadhan Rakhmat Sani S. Sukamto, Titien Sasono Wibowo Sejati, Priska Trisna Senata, Denny Sendi Novianto Sirait, Tamsir Hasudungan Sri Winarno Suharnawi Suharnawi Sukamto, Titien Sukamto, Titien S. Sukamto, Titien Suhartini Tacharri, Chusnuut Wijayanti, Selvi Zami, Farrikh Al Zulfiningrumi, Rahmawati