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All Journal The Journal of Experimental Life Sciences (JELS) Jurnal EECCIS PYTHAGORAS: Jurnal Program Studi Pendidikan Matematika Jurnal Saintek Lahan Kering Variance : Journal of Statistics and Its Applications Publik: Jurnal Manajemen Sumber Daya Manusia, Administrasi dan Pelayanan Publik Jurnal Sibermas (Sinergi Pemberdayaan Masyarakat) JAMBURA JOURNAL OF PROBABILITY AND STATISTICS International Journal of Health, Economics, and Social Sciences (IJHESS) Griya Journal of Mathematics Education and Application Parameter: Journal of Statistics Jurnal Info Sains : Informatika dan Sains Jurnal Pijar MIPA Research in the Mathematical and Natural Sciences Jurnal Riset Mahasiswa Matematika Journal of Artificial Intelligence and Digital Business Jurnal Ilmiah Ekonomi dan Manajemen Journal of Mathematics, Computation and Statistics (JMATHCOS)
Siti Nurmardia Abdussamad
Department Of Statistics, Universitas Negeri Gorontalo
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Mathematics Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Other

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Pemodelan Matematis Besaran Pengaruh pada Kasus Keputusan Pembelian Konsumen Gerai Rumah Karawo Abdussamad, Siti Nurmardia
Saintek Lahan Kering Vol 7 No 1 (2024): JSLK JUNI 2024
Publisher : Fakultas Pertanian, Universitas Timor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32938/slk.v7i1.2551

Abstract

The development of the business world has advanced very rapidly in the current era of globalization, where there is a lot of competition between companies that have the same business. One of the businesses that developed from the creations of children from the Gorontalo area is Karawo. Karawo is a symbol of Gorontalo's cultural identity, to this day not only has consumers in its own area but consumers outside the Gorontalo area. So it is important for companies to pay attention to attractiveness in terms of consumer purchasing decisions. Several factors that influence purchasing decisions include buyer awareness of a brand, product price and perceived quality. By using a mathematical model using a quantitative approach with a regression analysis method, it can describe whether there is an influence of brand awareness, price and perceived quality on consumer purchasing decisions at Gerai Rumah Karawo and the magnitude of the influence. The data collection technique was by distributing questionnaires to 68 respondents. The research results show that brand awareness, price and perceived quality simultaneously influence consumer purchasing decisions at the Rumah Karawo Outlet by 69.5%.
Comparison Of Radial Basis Function Neural Networks (RBFNN) And Autoregressive Moving Average (ARMA) Algorithms On Inflation Rate Prediction Models In Batam City Widya Reza; Febrya Christin Handayani Buan; Siti Nurmardia Abdussamad
Jurnal Info Sains : Informatika dan Sains Vol. 14 No. 02 (2024): Informatika dan Sains , Edition, June 2024
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/infosains.v14i02.4713

Abstract

The inflation rate in the city of Batam from January 2023 to April 2024 continues to fluctuate, so an accurate prediction model is needed so that inflation control can be carried out optimally. In this study, we conducted a comparative analysis between the Radial Basis Function Neural Network (RBFNN) method and the Autoregressive Moving Average (ARMA) model in predicting the inflation rate. The data used is historical data on the inflation rate of Batam City from January 2009 to April 2024. The results of the analysis show that the RBFN method with an MSE value of 0.239 is able to provide a more accurate prediction compared to the ARMA model (2.3) with an MSE value of 0.246 in predicting the inflation rate in Batam City. This is due to the RBFN's ability to capture complex and non-linear patterns contained in inflation data. In addition, the performance of RBFNN is also affected by the number of neurons and the basis function used. Thus, the results of this study show that the RBFN method can be an effective and efficient alternative in predicting the inflation rate in Batam City.
Pemodelan Faktor-Faktor Yang Mempengaruhi Perilaku Konsumen Pia Jagung Dumati menggunakan Structural Equation Modeling-Partial Least Square Abdussamad, Siti Nurmardia; Naue, Siti Nurmeylisya; Hasan, Nadia Kasmin
Research in the Mathematical and Natural Sciences Vol. 4 No. 1 (2025): November 2024-April 2025
Publisher : Scimadly Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55657/rmns.v4i1.192

Abstract

The development of industrial competition requires producers to continue to evaluate and innovate the products they produce. This research aims to analyze consumer behavior by looking at the factors that influence the purchase of Pia Jagung Dumati products in Gorontalo Regency. The method used is Structural Equation Modeling (SEM)-Partial Least Square (PLS) to provide a clearer picture of the influence on consumer behavior. Questionnaires distributed to 100 respondents became the data used in this research. The sampling technique used is purposive sampling. This research uses the variables price, product quality, promotions, and working hours. The results of the analysis are that the price, product quality and promotion variables have a significant influence on consumer behavior, while the working hours variable has no significant influence. This research provides recommendations for manufacturers to focus on flavor innovation, packaging and more creative digital marketing strategies to attract the attention of consumers, especially among the productive age group
Anxiety Contributing Factors in College Students during the Final Project: Ordinal Logistic Regression Analysis Tiarawati, Ni Wayan; Yahya, Lailany; Adityaningrum, Amanda; Mahdang, Putri Ayuningtias; Arsad, Nikmatisni; Abdussamad, Siti Nurmardia; Payu, Muhammad Rezky Friesta; Nasib, Salmun K.
International Journal of Health, Economics, and Social Sciences (IJHESS) Vol. 7 No. 1: January 2025
Publisher : Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/ijhess.v7i1.6964

Abstract

The prevalence of anxiety disorders among university students is high, particularly during periods of high stress, such as when students are completing their final projects. This research aimed to investigate how self-confidence and anxiety levels relate among students working on their final projects at the Statistics Department at Universitas Negeri Gorontalo. Research was conducted on 86 students aged 21-26 years old using an online questionnaire. Self-confidence, self-efficacy, and social support were independent variables, while anxiety levels (mild, moderate, and severe) were dependent variables. Self-confidence was found to be significantly correlated with anxiety levels, while self-efficacy and social support were not significantly correlated. The result of ordinal logistic regression analysis indicated that students with high self-confidence were 0.13 times more likely to experience mild or moderate anxiety compared to those with moderate self-confidence. Those with high levels of self-confidence, however, are more likely to suffer from severe anxiety (26%) than those with moderate levels of self-confidence (4%). In certain academic situations, high self-confidence may not be a hindrance against anxiety. A more comprehensive understanding of anxiety will require further research considering additional factors that contribute to anxiety, factors that were not considered in this study.
ANALISIS KUANTITATIF IMPLEMENTASI KEBIJAKAN PROGRAM BPJS KESEHATAN DI RSUD PROF. DR. ALOEI SABOE KOTA GORONTALO Abdussamad, Siti Nurcahyati; Abdussamad, Siti Nurmardia
Publik: Jurnal Manajemen Sumber Daya Manusia, Administrasi dan Pelayanan Publik Vol. 12 No. 1 (2025): Publik: Jurnal Manajemen Sumber Daya Manusia, Administrasi, dan Pelayanan Publ
Publisher : Universitas Bina Taruna Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37606/publik.v12i1.1712

Abstract

This research aims to determine the extent to communication and resources have on the effectiveness of the implementation of the BPJS Health program, both simultaneously and partially. The background to the problem of implementing the BPJS Health program is that for approximately 5 years, the BPJS Health program has always experienced a budget deficit. The main cause is the categories of BPJS Health participants who tend not to pay premiums on time and participants who only pay contributions when he just wanted to go to the hospital for treatment. This research uses quantitative research methods. The data collection technique is by distributing questionnaires to 100 samples who are BPJS Health users at RSUD Prof. Dr. Aloei Saboe, Gorontalo City. The research results show that communication and resources have a partial positive and significant influence of 21.8% and 62% respectively on the effectiveness of the implementation of the BPJS Health program and simultaneously the influence of communication and resources on the effectiveness of implementing the BPJS Health program was 61.3%.
Klasifikasi Tingkat Depresi Mahasiswa Menggunakan Image Recognition dengan Support Vector Machine Abdussamad, Siti Nurmardia; Doholio, Nadya Pratiwi; Lasaleng, Wahyu Pratama; Usia, Putu Ayu Indah N.; Rahman, Mohamad Iswanto; Adam, Dwi Putri Juniar
Research in the Mathematical and Natural Sciences Vol. 4 No. 1 (2025): November 2024-April 2025
Publisher : Scimadly Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55657/rmns.v4i1.193

Abstract

Mental health problems in Indonesia are increasing, with university students being one of the groups vulnerable to depression due to academic pressure, social expectations, and exposure to negative information. Early detection of depression still relies on questionnaire methods that have limitations in objectivity and accuracy. Therefore, this research aims to develop a classification system for student depression using image recognition technology with Support Vector Machine (SVM). The system analyses students' facial expressions and combines them with questionnaire results to improve the accuracy of early depression detection. The results showed that out of 131 respondents, 74% experienced moderate depression, with academic pressure as the main factor. This finding is consistent with the condition of final-year students who face high academic loads. With this method, early detection of depression is more accurate than conventional methods, which can help intervene more quickly in dealing with student mental health crises.
Pemilihan Metode Optimal Untuk Prediksi Angka Kemiskinan Di Provinsi Gorontalo: Perbandingan Double Exponential Smoothing dan Bayesian Structural Time Series wolah, Meitasya; Nasib, Salmun K.; Arsal, Armayani; Hasan, Isran K.; Asriadi; Abdussamad, Siti Nurmardia
Research in the Mathematical and Natural Sciences Vol. 4 No. 1 (2025): November 2024-April 2025
Publisher : Scimadly Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55657/rmns.v4i1.202

Abstract

Kajian ini mengevaluasi angka kemiskinan di Indonesia yang masih tinggi dengan fokus pada Provinsi Gorontalo yang menjadi urutan kelima sebagai provinsi termiskin di Indoneisa. Meskipun angka kemiskinan ekstrem nasional menurun menjadi 1,12% pada Maret 2023, Gorontalo mencatat masih 183,71 ribu penduduk miskin dengan garis kemiskinan per kapita sebesar Rp 442.194. Tujuan penelitian ini untuk membandingkan dua teknik peramalan, yaitu Bayesian Structural Time Series (BSTS) dan Double Exponential Smoothing (DES) untuk menilai efektivitas masing-masing metode dalam memprediksi angka kemiskinan di Provinsi Gorontalo. Hasil analisis menunjukkan bahwa model Double Exponential Smoothing (DES) memiliki Mean Absolute Percentage Error (MAPE) sebesar 6,6%, lebih rendah dibandingkan MAPE Bayesian Structural Time Series (BSTS) yang mencapai 7,39%. MAPE yang lebih rendah pada Double Exponential Smoothing (DES) menunjukkan kemampuannya yang lebih baik dalam mengidentifikasi pola data dan menghasilkan perkiraan yang lebih akurat. Meskipun BSTS mampu menangkap komponen musiman dan Trend dengan teknik probabilistik yang canggih, hasil ini menegaskan bahwa Double Exponential Smoothing (DES) adalah metode yang lebih efektif untuk memprediksi angka kemiskinan di Provinsi Gorontalo.
Comparison of Word2vec and CountVectorizer with Mutual Information in Support Vector Machine (SVM) for Public Sentiment Analysis Doholio, Nadya Pratiwi; Hasan, Isran K; Abdussamad, Siti Nurmardia
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6640

Abstract

Social media is widely used today. Along with the development of social media, it makes it not only a means of communication but also a means of exchanging opinions. One of the social media that is widely used to exchange opinions is X (Twitter). X is widely used to express opinions, particularly on controversial issues, such as the relocation of IKN. Therefore, sentiment analysis is needed to analyse public opinion regarding this national issue. SVM is widely used to classify sentiment based on several required categories, such as positive or negative. However, SVM will work even more effectively if the features used have good quality. Therefore, feature extraction and selection are necessary to enhance SVM classification accuracy. The selection of appropriate feature extraction is very important for classification. Therefore, this study aims to compare two feature extractions, namely Word2Vec and CountVectorizer by adding Mutual Information feature selection to SVM in classifying public sentiment from X. The results show that SVM with Word2Vec and CountVectorizer is more effective than SVM with Mutual Information feature selection. The results show that SVM with Word2Vec feature extraction and Mutual Information feature selection is more effective overall with 84% accuracy, 90% precision, 90% recall, and 90% f1-score, compared to SVM with CountVectorizer feature extraction and Mutual Information feature selection which has 80% accuracy, 83% precision, 92% recall, and 87% f1-score.
Evaluation of the Adaptive Fuzzy Neuro Inference System and Fuzzy Model Time Series Markov Chains in Forecasting Crude Oil Prices Hinelo, Ikrar Prasetyo; Nuha, Agusyarif Rezka; Hasan, Isran K; Nasib, Salmun K; Abdussamad, Siti Nurmardia
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6763

Abstract

The development of a country's economy is greatly influenced by global economic conditions, given the increasingly close links between countries through economic relations and international cooperation. One of the main factors in economic growth is international trade, particularly export and import activities. Crude oil is one of the most actively traded commodities. Given the highly volatile crude oil market, accurate price forecasts are crucial in economic and financial decision-making. This study compares the performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy Time Series Markov Chain (FTSMC) in forecasting the price of West Texas Intermediate (WTI) crude oil using time series data from 2020 to 2024 with saturated sampling technique. The implementation of both methods is carried out through Matlab Online and R-Studio software, with results showing that ANFIS has higher accuracy than FTSMC, as evidenced by the Mean Absolute Percentage Error (MAPE) value of 1,8010% for ANFIS and 3,7567% for FTSMC. Further analysis shows that ANFIS with a triangular membership function as well as significant lags at lag 1, lag 3, lag 4, and lag 7 is able to produce more accurate predictions and match the trend of actual data. Therefore, ANFIS is recommended as a more effective method in forecasting WTI crude oil prices, which can provide valuable insights for policy makers and industry stakeholders.
CFGWC-PSO in Analyzing Factors Affecting the Spread of Dengue Fever in East Java Province Abdussamad, Siti Nurmardia; Astutik, Suci; Effendi, Achmad
The Journal of Experimental Life Science Vol. 9 No. 3 (2019)
Publisher : Graduate School, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1060.997 KB) | DOI: 10.21776/ub.jels.2019.009.03.10

Abstract

Fuzzy Geographically Weighted Clustering-Particle Swarm Optimization using Context Based Clustering (CFGWC-PSO) has been developed to clustering in factors influencing the spread of dengue fever in East Java Province. CFGWC-PSO method can overcome slow computing time problems in terms of iterations, and produce accurate data partition with stable. In this research, CFGWC-PSO applied to 11 variables from data on the causes of the spread of dengue fever in East Java Province in 2017. CFGWC-PSO using the FCM method to determine the context variable. Processing used the results of clustering with 2 clusters until 5 clusters. From the three validation index that used to find out the right number of clustering, two clusters gave better clustering results. CFGWC-PSO shows that all districts/cities in cluster 2 become dengue fever endemic areas that need to be considered by the East Java Provincial Government.Keywords: Context-Based Clustering, dengue hemorrhagic fever, Fuzzy Geographically Weighted Clustering-Particle Swarm Optimization.
Co-Authors Abdussamad, Siti Nurcahyati Adam, Dwi Putri Juniar Agustina, Melisa Agusyarif Rezka Nuha Alaina, Silvana Rahmayanti Armayani Arsal Asriadi Asriadi Asriadi Djafar, Fikriyanto Djihad Wungguli Doholio, Nadya Pratiwi effendi, Achmad Febrya Christin Handayani Buan Hasan, Nadia Kasmin Hinelo, Ikrar Prasetyo Ikhlas Ul Aqmal Ilato, Mutiara Ismail Djakaria Isran K Hasan Juriko Abdussamad Karim, Finansiya S. Abd. La Ode Nashar Lailany Yahya Lasaleng, Wahyu Pratama Marukai, Nur Amalia Naue, Siti Nurmeylisya Nikmatisni Arsad NISKY IMANSYAH YAHYA Pakaya, Desya Neydi Putri Putri Ayuningtias Mahdang Rahman, Mohamad Iswanto Rahmi, Emli Salmun K. Nasib Suci Astutik Sulista Kamah Taufik, Mohamad Alfiransyah Tiarawati, Ni Wayan Usia, Putu Ayu Indah N. Usman, Rizal Wewengkang, Hanz Franklyn Bachruddin Widya Reza Widya Reza wolah, Meitasya Zuchri Abdussamad