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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang BAREKENG: Jurnal Ilmu Matematika dan Terapan JITK (Jurnal Ilmu Pengetahuan dan Komputer) Variance : Journal of Statistics and Its Applications Buletin Ilmiah Sarjana Teknik Elektro Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Statistika dan Aplikasinya Journal Of Human And Education (JAHE) Journal of Data Insights Jurnal Pengabdian Masyarakat dan Riset Pendidikan Scientific Journal of Computer Science
Ariska Fitriyana Ningrum
Universitas Muhammadiyah Semarang
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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HYBRID VECTOR AUTOREGRESSIVE AND LONG SHORT TERM MEMORY MODEL FOR PREDICTING ECONOMIC GROWTH INDICATORS IN INDONESIA: A COMPARISON OF ADAM, NADAM, AND RMSPROP OPTIMIZATION METHODS Ningrum, Ariska Fitriyana; Khaira, Mulil
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1139-1154

Abstract

This study aims to compare the performance of three optimization methods—Adam, Nadam, and RMSProp—in forecasting monthly economic indicators of Indonesia, namely the Consumer Price Index (CPI), Inflation, and Gross Domestic Product (GDP), using a hybrid Vector Autoregressive–Long Short-Term Memory (VAR–LSTM) model. The analysis begins with Vector Autoregression (VAR), where VAR(4) is selected as the best model based on the lowest Akaike Information Criterion (AIC) value of 1.075. Significant parameters from the VAR model are then used as input variables for the LSTM to enhance forecasting accuracy. The experimental results show that all three optimization methods generate similar prediction patterns, with forecasted values closely tracking the actual data. Nevertheless, the best optimizer differs across variables: Nadam performs best for CPI with a Root Mean Square Error (RMSE) of 0.4996, Adam yields the best performance for Inflation with an RMSE of 0.676, and RMSProp performs best for GDP with an RMSE of 1.288. Despite these variations, the overall forecasting performance of the three methods is comparable. These findings indicate that the VAR–LSTM approach can effectively capture the dynamic patterns of multiple economic variables and that the choice of optimization method should be aligned with the specific characteristics of the data, considering both accuracy and computational efficiency.
Identification of Dominant Topics in Public Discussions on IKN using Latent Dirichlet Allocation (LDA) and BERTopic Ningrum, Ariska Fitriyana; Talirongan, Florence Jean B.; Tangaro, Diana May Glaiza G.
Scientific Journal of Computer Science Vol. 1 No. 1 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i1.2025.19

Abstract

This study aims to analyze public opinion related to the relocation of Indonesia's National Capital City (IKN) through topic modeling on Twitter data. The two main approaches used are Latent Dirichlet Allocation (LDA) based on Bag of Words and BERTopic based on Transformer language model. LDA was chosen for its ability to identify topic distribution in large text collections, while BERTopic was used to overcome the limitations of LDA in capturing semantic meaning in short and informal texts such as tweets. The analysis was conducted on a collection of tweets discussing the relocation of IKN, with the aim of uncovering the main themes and public perceptions. The result of LDA showed three main topics in the public discussion, namely (1) political debate and nationalism related to the relocation, (2) policy implementation and project execution, and (3) economic justification and challenges facing Jakarta. Mean-while, BERTopic identified topics with more contextual representations, including aspects of investment, economic impact construction progress, and public perception. Dominant topics include urban relocation, investment in IKN, and socio-economic impacts. The novelty of study lies in the comparison of two topic modeling approaches in the context of social media sentiment analysis related to major public policy issues. These findings not only enrich the understanding of the narratives that develop in society, but also provide important insights for policy makers in responding to public opinion more appropriately and contextually.
Analysis of Suspected Factors in Tuberculosis Cases in Semarang City Using a Logistic Regression Model Amri, Ihsan Fathoni; Rohim, Febrian Hikmah Nur; Ardiansyah, Muhammad Ivan; Saputra, Farid Sam; Supriyanto; Ningrum, Ariska Fitriyana; Nakib, Arman Mohammad
Scientific Journal of Computer Science Vol. 1 No. 1 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i1.2025.32

Abstract

Tuberculosis (TB) is one of the world's deadliest infectious diseases, with Indonesia being among the countries with the highest TB burden. Semarang City, as an urban area with a dense population, faces significant challenges in controlling TB, particularly among vulnerable populations. This study identifies significant risk factors influencing TB incidence in Semarang City using a binary logistic regression model. Descriptive analysis reveals an imbalance in the data, with the majority of patients categorized as "not indicated for TB." Chi-Square tests show that variables such as shortness of breath, persistent fever for more than one month, diabetes mellitus, and household contact are significantly associated with TB incidence. The logistic regression model demonstrates overall significance (G statistic = 275.13; p-value = 1.23×10−55), with shortness of breath and diabetes mellitus emerging as major risk factors based on odds ratio interpretation. However, the model's performance in detecting the "indicated for TB" category is very low (Precision 36.36%; Recall 2.05%; F1-Score 3.88%), despite an overall accuracy of 87.25%. The poor performance in the "1" category and the Pseudo R2 value of 7% are likely related to data imbalance, where the number of cases in the "1" category is much smaller than in the "0" category, leading to bias toward the majority class. Additionally, the distribution of predictor variables that do not provide sufficient information to distinguish the "1" category from the "0" category further contributes to the model's limited ability to explain data variability overall.
Waiting Time Analysis of Willingness to Pay for Rice Farming Insurance Premiums Using Cox Proportional Hazard Modeling and Weibull Method Mutiah, Siti; Bisoumi, Yan Nazala; Nudyawati, Elsa; Daud, Khamidah Arsyad; Nisa, Rofiah Ainun; Sulistiani, Dwi; Amri, Ihsan Fathoni; Ningrum, Ariska Fitriyana; Mostfa, Ahmed A.
Scientific Journal of Computer Science Vol. 1 No. 1 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i1.2025.34

Abstract

Rice is a primary commodity in Indonesia's agricultural sector but is highly vulnerable to climate risks such as floods, droughts, and pest infestations. To mitigate these risks, the government, in collaboration with PT. Asuransi Jasa Indonesia (Jasindo), launched the Rice Farming Insurance Program (AUTP) in 2015. This study aims to analyze the willingness-to-pay time of farmers for AUTP premiums in Jayaraksa Village, Cimaragas Subdistrict, Ciamis Regency, using Weibull regression and Cox Proportional Hazard models. Factors such as education, secondary employment, rice production, and farming costs were examined to understand their influence on farmers' participation. Based on the analysis, the Weibull regression model, with a lower AIC value compared to Cox Proportional Hazard (270.4431 vs. 330.9111), demonstrated better performance in explaining the data. This research contributes to the development of more effective AUTP policies by identifying key factors influencing farmers' participation.
Literasi Data Kependudukan melalui Infografis dan Monografi Desa Bagi Kader Posyandu : Pengabdian Utami, Tiani Wahyu; Ningrum, Ariska Fitriyana; Imron, Ali; Nurhidayah, Sri; Yunanita, Novia
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.5483

Abstract

Literasi data kependudukan menjadi sangat penting karena menyangkut kemampuan individu, organisasi, maupun pemerintah dalam memahami, menggunakan, dan mengelola informasi mengenai jumlah, karakteristik, serta dinamika penduduk. Bagi kader Posyandu, data kependudukan memiliki arti strategis karena mereka adalah ujung tombak layanan kesehatan dasar di tingkat masyarakat. Tanpa pencatatan yang baik, intervensi menjadi tidak terarah dan evaluasi program menjadi sulit dilakukan. Kelurahan Pedurungan Lor, khususnya RW 1, Kecamatan Pedurungan, Kota Semarang, dipilih sebagai lokasi kegiatan pengabdian masyarakat. Urgensi kegiatan pengabdian ini adalah mengingat pentingnya data sebagai dasar dalam pengambilan keputusan dan perencanaan kesehatan masyarakat, juga peningkatan kompetensi kader Posyandu dalam pengelolaan dan visualisasi data menjadi sangat mendesak. Oleh karena itu diperlukan kegiatan yang meningkatkan pengetahuan literasi data kependudukan dan melatih pembuatan infografis dan monografi desa. Evaluasi dilakukan dengan pre-post test terkait pemahaman literasi data dan pembuatan infografis. Berdasarkan kegiatan yang dilakukan menunjukkan hasil adanya peningkatan pemahaman literasi data dan pembuatan infografis melalui Canva, hal ini tercermin pada nilai rata-rata skor post test yang mengalami peningkatan yang signifikan.
EVALUATING CLUSTERING METHODS FOR SEMANTIC REPRESENTATION OF DISASTER NEWS USING BERT EMBEDDINGS AND HBDSCAN Ningrum, Ariska Fitriyana; Purwanto, Dannu; Sharkawy, Abdel Nasser
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7204

Abstract

Natural disasters that frequently occur in Indonesia demand a fast and accurate information monitoring and analysis system through online news sources. This study aims to identify topic patterns related to natural disasters in Indonesia using news articles from Detik.com through a semantic clustering approach. A total of 1,000 articles were collected, preprocessed, and represented using the Sentence-BERT (SBERT) model to capture contextual relationships between sentences. The vector representations were then clustered using three methods: K-Means, Agglomerative Hierarchical Clustering, and HDBSCAN. The performance of each method was evaluated using the Silhouette Score, Davies–Bouldin (DB) Index, and Calinski–Harabasz (CH) Index. The results show that HDBSCAN achieved the best performance with a Silhouette Score of 0.215, a DB Index of 1.557, and a CH Index of 18.102, outperforming Agglomerative (0.028, 3.945, 29.669) and K-Means (0.055, 3.678, 36.778). Moreover, the HDBSCAN model achieved the highest coherence score of 0.8669, indicating strong semantic consistency within clusters. Five coherent clusters emerged, representing major disaster themes: landslides, earthquakes, tornadoes, flash floods, and volcanic activity. The visualization of word clouds for each cluster reinforced the interpretation of these disaster topics. Overall, the combination of SBERT and HDBSCAN effectively groups news articles based on semantic similarity. These findings highlight the potential of Natural Language Processing (NLP) to enhance data-driven media monitoring, support early warning systems, and strengthen disaster communication and mitigation strategies in Indonesia
Co-Authors Alfian Ma’arif Ali Imron Alwan Fadlurohman Amri, Saeful Anggun Lestari, Febi Asno Azzawagama Firdaus Aura Hisani, Zahra Bisoumi, Yan Nazala Dannu Purwanto Daud, Khamidah Arsyad Dhani, Oktaviana Rahma Dwi Sulistiani Furizal, Furizal Ihsan Fathoni Amri Kariyamin, Kariyamin Khaira, Mulil Kusuma Wardani, Amelia M. Al Haris Mostfa, Ahmed A. Muhammad Ivan Ardiansyah Musa, Fitri Diana mutiah, siti Nakib, Arman Mohammad Nisa, Rofiah Ainun Nudyawati, Elsa Nurhidayah, Sri Prizka Rismawati Arum Rahma Dhani, Oktaviana Rohim, Febrian Hikmah Nur Saputra, Farid Sam Sharkawy, Abdel Nasser Sitti Sahara Supriyanto Talirongan, Florence Jean B. Tangaro, Diana May Glaiza G. Tiani Wahyu Utami Wardani, Amelia Kusuma Wijaya, Setiawan Ardi Yunanita, Novia Yusrin, Yusrin Zahra Aura Hisani