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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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Sentiment Analysis of Public Opinion on Handling Stunting in Indonesia using Random Forest Ningrum, Ariska Fitriyana; Ihsan Fathoni Amri; Zahra Aura Hisani
Jurnal Statistika dan Aplikasinya Vol. 8 No. 1 (2024): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08103

Abstract

The issue of stunting is important to address, as it has the potential to affect the human resource potential and is related to health levels, and even child mortality. The Indonesian government targets to reduce the stunting rate to 14 percent by 2024 through an accelerated stunting reduction program as an effort to improve the nutritional status of the society and also reduce the prevalence of stunting or stunted children. Understanding public sentiment towards the stunting initiative is crucial for policymakers and stakeholders to design effective interventions and allocate resources efficiently. This study aims to analyze public sentiment related to stunting in Indonesia, which impacts children's growth and development. Through the use of sentiment analysis techniques, this study aims to understand public perceptions and attitudes towards the issue of stunting, evaluating whether the general sentiment is positive, negative or neutral. The results of this analysis are expected to provide useful insights for policymakers and health practitioners in designing and implementing more effective strategies to address the issue of stunting. This study conducted sentiment analysis from crawled Twitter data, showing positive and negative sentiments of the public regarding stunting handling in Indonesia. Furthermore, classification analysis using random forest was conducted and resulted in an accuracy score of 97.5%. The model is good enough but, we suggest trying other algorithms in further research.
CLASSIFICATION OF MYPERTAMINA APP REVIEWS USING SUPPORT VECTOR MACHINE Fadlurohman, Alwan; Yunanita, Novia; Rohim, Febrian Hikmah Nur; Wardani, Amelia Kusuma; Ningrum, Ariska Fitriyana
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss2page223-228

Abstract

Indonesia is rich in natural resources, including oil and gas, and it manages these strategic assets through state-owned enterprises, one of which is PT Pertamina. Pertamina is responsible for domestic fuel production, distribution, and price stabilization. To improve efficiency and transparency, Pertamina developed the MyPertamina application that enables cashless fuel purchases, stock monitoring, and up-to-date price information. The application aims to streamline distribution and control fuel prices, thus helping to stabilize the cost of goods and services. MyPertamina also ensures subsidized fuel distribution is more effective and targeted by identifying and verifying subsidy recipients, reducing the potential for abuse. A sentimental analysis of subsidized fuel user reviews using this application is needed to understand the public's views. This research uses the Support Vector Machine (SVM) method to analyze the sentiment of MyPertamina app reviews. This research produced a stable model. Out of 200 reviews, 190 were negative, and nine were positive, with an SVM model accuracy of 97%. Wordcloud visualization shows the words that appear frequently in each sentiment. Positive reviews appreciated the photo verification feature, easy payment, and good service. Negative reviews included verification difficulty, app error, and feature failure.
OPTIMIZATION OF NAÏVE BAYES USING BACKWARD ELIMINATION FOR HEART DISEASE DETECTION Amri, Saeful; Ningrum, Ariska Fitriyana; Arum, Prizka Rismawati
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 2 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.11.2.2023.44-50

Abstract

Heart disease is the main cause of death in humans. Even though preventive measures have been taken such as regulating food (diet), lowering cholesterol, and treating weight, diabetes, and hypertension, heart disease remains a major health problem. There are several factors that cause heart disease, including age, type of chest pain, high blood pressure, sugar levels, ECG test values, maximum heart rate, and induced angina. To reduce the percentage of deaths due to heart disease, we need a system that can predict heart disease. The algorithm used in this research is a combination of the Backward Elimination and Naive Bayes algorithms to increase accuracy in diagnosing heart disease. According to the results of this research, the Naive Bayes algorithm has an accuracy value of 78.90% and an Area Under Curve (AUC) value of 0.86, which is included in the good classification category. Combining the Backward Elimination and Naïve Bayes algorithms has an accuracy value of 82.31% and an Area Under Curve (AUC) value of 0.88.
Data Visualization Excellence: Google Data Studio Workshop At Sekolah Indonesia Kuala Lumpur Amri, Saeful; Fadlurohman , Alwan; Ningrum, Ariska Fitriyana; Purwanto, Dannu; Amri , Ihsan Fathoni; Wardani, Amelia Kusuma; Dhani, Oktaviana Rahma
Journal Of Human And Education (JAHE) Vol. 5 No. 1 (2025): Journal of Human And Education (JAHE)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jh.v5i1.2178

Abstract

The development of information technology and the entry of the industrial revolution 4.0 era has led to the inseparability of human activities related to the use of technology. In today's rapidly growing information age, data is one of the most valuable assets. The ability to collect, analyze, and interpret data is becoming a very important skill not only in the world of work but also in education. Education is the foundation for preparing future generations for increasingly complex global challenges, and a good understanding of data can provide a significant competitive advantage. In schools, the ability to analyze and interpret data is becoming an invaluable skill for students. Along with the development of technology, data visualization has become an effective method to convey information in a more comprehensible manner. In this context, Google Data Studio offers a powerful and easy-to-use tool for creating interactive dashboards that help in analyzing and presenting data. Indonesian Migrant Workers (TKI) are Indonesian citizens who live and work abroad. TKI provide a large contribution of foreign exchange to the country of Indonesia. However, there are problems in the field of education for children whose parents work as TKI in Malaysia, especially education that is relevant to success in terms of opening their own jobs abroad. This is considered important because to get jobs in government agencies or companies in Malaysia, the children of TKI working in Malaysia must compete with job seekers who are Malaysian citizens. One alternative that can be taken to overcome competition in getting jobs is to create your own jobs. Opening your own jobs is not an easy thing. Knowledge and insight about this are needed which are given early on to the children of TKI in school. By teaching Google Data Studio in the form of data visualization to students, they not only learn how to read and interpret graphs and diagrams, but also how to present their own data in a more interesting and informative way. This ability will be very useful in the future, both in academic and professional environments. By providing insight into Google Data Studio to students in schools, these students have the provisions to be able to read data and have the opportunity to work and get decent jobs. As a Community Service activity with an international scope, this activity takes partners in Malaysia, namely the Indonesian School-Kuala Lumpur - SIKL which is located in Sentul, Kuala Lumpur, Federal Territory of Kuala Lumpur. The Community Service Team of Muhammadiyah University of Semarang is very receptive to criticism and suggestions so that the implementation of Community Service in the future can be even better.
Understanding Time Series Forecasting: A Fundamental Study Furizal, Furizal; Ma’arif, Alfian; Kariyamin, Kariyamin; Firdaus, Asno Azzawagama; Wijaya, Setiawan Ardi; Nakib, Arman Mohammad; Ningrum, Ariska Fitriyana
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13318

Abstract

Time series forecasting plays a vital role in economics, finance, engineering, etc., due to its predictive power based on past data. Knowing the basic principles of time series forecasting enables wiser decisions and future optimization. Despite its importance, some researchers and professionals find it difficult to use time series forecasting techniques effectively, especially with complex data settings and selection of methods for a particular problem. This study attempts to explain the subject of time series forecasting in a comprehensive and simple manner by integrating the main stages, components, preprocessing steps, popular forecasting models, and validation methods to make it easier for beginners in the field of study to understand. It explains the important components of time series data such as trend, seasonality, cyclical components, and irregular components, as well as the importance of data preprocessing steps, proper model selection, and validation to achieve better forecasting accuracy. This study offers useful material for both new and experienced researchers by providing guidance on time series forecasting techniques and approaches that will help in enhancing the value of decision making.
PENGOPTIMALAN KETERAMPILAN DIGITAL SISWA SMAN 01 KEMBANG MELALUI PEMBUATAN GOOGLE FORM DAN ANALISIS DATA Ningrum, Ariska Fitriyana; Yusrin, Yusrin; Yunanita, Novia; Rohim, Febrian Hikmah Nur
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 5 (2024): Vol. 5 No. 5 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i5.34842

Abstract

Penguasaan teknologi informasi merupakan keterampilan penting dalam menghadapi era digital, terutama bagi siswa sekolah menengah atas (SMA). Kegiatan pengabdian masyarakat ini berfokus pada peningkatan literasi digital siswa kelas 11 SMAN 01 Kembang melalui pelatihan pembuatan Google Form dan analisis data. Tujuan utama dari kegiatan ini adalah membekali siswa dengan keterampilan praktis dalam menggunakan Google Form untuk pengumpulan dan analisis data yang mendukung kegiatan akademik. Dalam workshop, siswa diperkenalkan pada fungsi Google Form, mulai dari cara membuat form, memilih jenis pertanyaan, hingga teknik dasar analisis data. Dengan metode pembelajaran interaktif, siswa diajak untuk langsung mempraktikkan setiap langkah dalam pembuatan formulir online dan bereksperimen dengan berbagai fitur yang tersedia. Hasil dari kegiatan ini menunjukkan peningkatan kemampuan digital siswa, terbukti dari keberhasilan mereka dalam membuat dan menggunakan Google Form secara mandiri. Selain itu, workshop ini juga membantu memperkuat keterampilan berpikir analitis dan kritis siswa dalam menghadapi masalah berbasis data. Dampak jangka panjang yang diharapkan adalah kemampuan siswa dan sekolah untuk mengintegrasikan teknologi informasi secara lebih luas dalam proses pembelajaran, sekaligus mempersiapkan siswa untuk tantangan dunia digital yang semakin berkembang.
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.
Co-Authors Abdel Nasser Sharkawy Alfian Ma’arif Ali Imron Alwan Fadlurohman Amelia Kusuma Wardani Amri, Saeful Asno Azzawagama Firdaus Bisoumi, Yan Nazala Dannu Purwanto Daud, Khamidah Arsyad Dhani, Oktaviana Rahma Dwi Sulistiani Febi Anggun Lestari Fitri Diana Musa Furizal, Furizal Ihsan Fathoni Amri Kariyamin, Kariyamin Khaira, Mulil M. Al Haris Mostfa, Ahmed A. Muhammad Ivan Ardiansyah mutiah, siti Nakib, Arman Mohammad Nisa, Rofiah Ainun Nudyawati, Elsa Nurhidayah, Sri Oktaviana Rahma Dhani Prizka Rismawati Arum Rohim, Febrian Hikmah Nur Saputra, Farid Sam 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