Articles
<![CDATA[Penerapan Metode Double Moving Average Untuk Meramalkan Hasil Produksi Tanaman Padi di Provinsi Gorontalo ]]>
<![CDATA[Hendra Andrianto Yusuf]]>;
<![CDATA[Ismail Djakaria]]>;
<![CDATA[Resmawan Resmawan]]>
<![CDATA[ d\'Cartesian: Jurnal Matematika dan Aplikasi Vol 9, No 2 (2020): September 2020 ]]>
Publisher : <![CDATA[Sam Ratulangi University ]]>
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DOI: 10.35799/dc.9.2.2020.28377
<![CDATA[Artikel ini membahas tentang metode double moving average untuk mengetahui hasil ramalan produksi tanaman padi di Provinsi Gorontalo. Metode double moving average merupakan metode rata-rata bergerak linier yang digunakan untuk mengatasi data deret waktu dengan pola yang cenderung mengalami trend linear. Berdasarkan pola data hasil produksi tanaman padi, menunjukkan bahwa pola data tersebut mengalami peningkatan setiap tahunnya dan dapat diidentifikasi bahwa data berpola trend. Hasil penelitian ini menunjukan bahwa model terbaik untuk meramalkan hasil produksi tanaman padi diperole MA (2 × 2) dengan model persamaan adalah F18+p =331692+(-5373) × m dan nilai tingkat akurasi yaitu measure absolute persenrage error (MAPE) sebesar 5.3537. Sehingga diperoleh hasil peramalan 5 tahun ke depan yaitu tahun 2019 sebesar 326318.5 Ton, 2020 sebesar 32094.5 Ton, dan seterusnya sampai tahun 2023 sebesar 304826.5 Ton.]]>
<![CDATA[Perbandingan Metode K-Means Clustering dengan Self-Organizing Maps (SOM) untuk Pengelompokan Provinsi di Indonesia Berdasarkan Data Potensi Desa ]]>
<![CDATA[Iyohu, Lisa Rianti]]>;
<![CDATA[Ismail Djakaria]]>;
<![CDATA[La Ode Nashar]]>
<![CDATA[ Jurnal Statistika dan Aplikasinya Vol 7 No 2 (2023): Jurnal Statistika dan Aplikasinya ]]>
Publisher : <![CDATA[Program Studi Statistika FMIPA UNJ ]]>
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DOI: 10.21009/JSA.07208
<![CDATA[K-Means is a method of grouping data into several different groups so that data that has similar characteristics is made into one group while data that has different characteristics is made into a different group, where this method works by minimizing the distance between the data and the cluster center. In addition to K-Means clustering, there is also the Self Organizing Maps (SOM) method which is an undirected method, meaning that layers consisting of neurons are arranged into groups based on input values, where each data grouping process is based on the characteristics or features of the data. Clustering is carried out in Provinces in Indonesia based on village potential data in 2021 with the aim of knowing the performance comparison of K-Means clustering and Self Organizing Maps (SOM). Determination of the optimal number of clusters is carried out using the Elbow method, the results in the study obtained 3 clusters for both K-Means clustering and Self Organizing Maps (SOM). The clustering results are evaluated using the Davies Bouldin Index (DBI) value and show that clustering using the Self Organizing Maps (SOM) method provides better results than using the K-Means clustering method where the DBI value is 0.1829366. The clustering results using the Self Organizing Maps (SOM) method for cluster 1 consist of 31 province members, cluster 2 consists of 1 province member and cluster 3 consists of 2 province members.]]>
<![CDATA[Model Integer Linear Programming pada Optimisasi Distribusi Logistik di Daerah Bencana ]]>
<![CDATA[Kasim, Miranti H.]]>;
<![CDATA[Djakaria, Ismail]]>;
<![CDATA[Yahya, Lailany]]>
<![CDATA[ Research in the Mathematical and Natural Sciences Vol. 1 No. 1 (2022): November 2021-April 2022 ]]>
Publisher : <![CDATA[Scimadly Publishing ]]>
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DOI: 10.55657/rmns.v1i1.3
<![CDATA[Logistics distribution in disaster-affected areas includes the delivery of relief goods (food, medicine, and clothing) using three types of vehicles with different carrying capacities for each vehicle. In distributing relief goods, unfulfilled requests can occur in disaster-affected areas. This problem can be modeled in the form of integer linear programming because there is a variable that must be an integer value to minimize unfulfilled demand for all types of relief goods at each point of demand. The branch and bound method can solve this problem's integer linear programming model. The next model is simulated using the LINGO 11.0 programming language. The simulation results show that the system can be considered feasible to use. Thus, the vehicle allocation is also obtained in each period with distribution costs from the point of supply to the point of demand. This distribution process lasts for four periods by showing a change in the number of unfulfilled requests. In the last period, there was no more unfulfilled demand for aid to obtain optimal results.]]>
<![CDATA[Implementasi Model Cox Stratifikasi Interaksi dan Tanpa Interaksi untuk Mengidentifikasi Faktor-Faktor Laju Kesembuhan Pasien TB Paru ]]>
<![CDATA[Modeong, Fakhira]]>;
<![CDATA[Isa, Dewi Rahmawaty]]>;
<![CDATA[Djakaria, Ismail]]>;
<![CDATA[Payu, Muhammad Rezky Friesta]]>;
<![CDATA[Mahmud, Sri Lestari]]>
<![CDATA[ Research in the Mathematical and Natural Sciences Vol. 2 No. 2 (2023): May-October 2023 ]]>
Publisher : <![CDATA[Scimadly Publishing ]]>
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DOI: 10.55657/rmns.v2i2.130
<![CDATA[This study aims to determine the factors that most influence the rate of recovery of pulmonary tuberculosis patients using the Cox Proportional Hazard model. In the case of the cure rate of pulmonary tuberculosis patients, not all independent variables meet the proportional hazard assumption, so the stratified cox regression model is used. The stratified cox regression model used is the stratified cox model with interaction and without interaction involving pulmonary tuberculosis patients in one of the Gorontalo Hospitals. The results showed that the variables of shortness of breath, previous pulmonary tuberculosis patients, and smoking habits were the most significant factors affecting the recovery rate of pulmonary tuberculosis patients.]]>
<![CDATA[Analisis Kemampuan Penalaran Matematis Siswa pada Materi Pola Bilangan Ditinjau Dari Kemampuan Menyelesaikan Soal Berbasis Masalah ]]>
<![CDATA[Paputungan, Arsiullahnur R.]]>;
<![CDATA[Djakaria, Ismail]]>;
<![CDATA[Ismail, Yamin]]>
<![CDATA[ Research in the Mathematical and Natural Sciences Vol. 3 No. 2 (2024): May-October 2024 ]]>
Publisher : <![CDATA[Scimadly Publishing ]]>
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DOI: 10.55657/rmns.v3i2.162
<![CDATA[This research is a qualitative descriptive study which aims to determine students' mathematical reasoning abilities in solving problem-based questions with categories of high, sufficient and low mathematical reasoning abilities. The subjects studied were 18 students at SMPN 15 Gorontalo who were in class VIII. The data collection technique is a means of collecting data by researchers which takes place in two stages, namely tests and interviews. The indicators used in this research are as follows: 1) Ability to present mathematical statements in writing. 2) submit an opinion. 3) carry out mathematical manipulations. 4) compiling evidence, providing reasons or evidence for the truth and 5) drawing conclusions. The research results showed that the ability to present mathematical statements obtained a presentation of 68.52% in the high category, the ability to put forward conjectures in the presentation 44.44% in the sufficient category, the ability to manipulate mathematical presentations in the sufficient category 44.44%, the ability to draw conclusions, compile evidence, provide reasons or evidence for the correctness of the solution. obtained presents 48.15%. sufficient category, an interesting conclusion from the statement obtaining a percentage of 40.74% in the sufficient category.]]>
<![CDATA[Analisis Peluang Jangka Panjang Mesin Penggilingan Padi Menggunakan Rantai Markov ]]>
<![CDATA[Nasib, Salmun K.]]>;
<![CDATA[Hasan, Riyanto]]>;
<![CDATA[Djakaria, Ismail]]>;
<![CDATA[Payu, Muhammad Rezky Friesta]]>;
<![CDATA[Nuha, Agusyarif Rezka]]>;
<![CDATA[Nashar, La Ode]]>
<![CDATA[ Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi EULER: Volume 12 Issue 1 June 2024 ]]>
Publisher : <![CDATA[Universitas Negeri Gorontalo ]]>
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DOI: 10.37905/euler.v12i1.25280
<![CDATA[The reliability of the machinery greatly affects the long-term potential of the grinding of pepper in Mustika village, Paguyaman district, and Boalemo district. The smoothness of the production process depends heavily on the condition of the machine, and if the machine's reliability is disrupted, then it will affect production. The purpose of this study is to determine the probability of the steady state of the machine and the timing of the maintenance of the grinding machine in the Mustika Village of Boalemo district. The Markov chain is a method used to deal with the purpose, whereas the Markov chain is the method used for predicting future events. The final result was an ergodic transition chance matrix, resulting in an estimated best maintenance time of 28 days of use with a steady state chance of 62.27\% of the machine being in good condition, 27.8\% in mild damage, and 9.93\% in severe damage.]]>
<![CDATA[PERBANDINGAN METODE LVQ DAN BACKPROPAGATION UNTUK KLASIFIKASI STATUS GIZI ANAK DI KECAMATAN SANGKUP ]]>
<![CDATA[Alamri, Fahima]]>;
<![CDATA[Ningsih, Setia]]>;
<![CDATA[Djakaria, Ismail]]>;
<![CDATA[Wungguli, Djihad]]>;
<![CDATA[K. Hasan, Isran]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.3.314-321
<![CDATA[The problem of children nutrition isi still a problem in various regions in Indonesia. Poor or poor nutrition of children is influenced by several factors, namely insufficient food intake and infectious diseases. Undernutrition or poor nutrition can be known from the nutritional status assessment obtained from classifying the nutrional status of children. Classification is a part of data mining that is often used to classify data based on certain data or variables. This study aims to compare the classification of the nutritional status of children using data mining with the learning vector quantization (LVQ) and backpropagation methods. Test were carried out using a comparasion ratio of training and testing data, namely 75% and 25%. From the research results, LVQ is superior with an accuracy of 95.12% and backpropagation of 80.49%.]]>
<![CDATA[REACT STRATEGY ON MATHEMATICAL REASONING REVIEWED FROM STUDENTS’ INTEREST IN LEARNING MATHEMATICS ]]>
<![CDATA[Mahajani, Roy]]>;
<![CDATA[Abbas, Nurhayati]]>;
<![CDATA[Djakaria, Ismail]]>;
<![CDATA[Majid, Majid]]>
<![CDATA[ Daya Matematis: Jurnal Inovasi Pendidikan Matematika Vol 12, No 3 (2024): Desember ]]>
Publisher : <![CDATA[Universitas Negeri Makassar ]]>
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DOI: 10.26858/jdm.v12i3.66319
<![CDATA[This study aims to determine whether the REACT strategy effectively enhances students’ mathematical reasoning based on their interest in learning, specifically for students in class X at SMA Negeri 1 Asparaga. The study employed a quasi-experimental with a 2 × 2 treatment-by-level design, analyzed using two-way ANOVA and Tukey’s test. The findings are as follows: (1) The REACT strategy is more effective than conventional teaching methods in enhancing students’ interest in learning (Fcount = 27.230 > Ftable = 4.105) and average scores of 44.79 & 34.52; (2) There is a significant interaction between the teaching model and students’ interest in learning on their mathematical reasoning (Fcount = 7.534 > Ftable = 4.105); (3) Among students with high interest in learning, the REACT strategy outperforms the conventional model, with a significant result (calculated significance with SPSS v.23 = 1.000 > 0.05) and average scores of 57.50 & 44.11; (4) Among students with low interest in learning, the REACT strategy also demonstrates superiority over the conventional model in critical mathematical thinking, with a significant result (calculated significance with SPSS v.23 = 0.314 > 0.05) and average scores of 31.71 & 27.56.]]>
<![CDATA[Optimization of Interval Singh’s Fuzzy Time Series with Particle Swarm Optimization for Forecasting Consumer Price Index in Luwuk City ]]>
<![CDATA[siswanto, Navira]]>;
<![CDATA[Djakaria, Ismail]]>;
<![CDATA[Arsal, Armayani]]>
<![CDATA[ Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025) ]]>
Publisher : <![CDATA[Jurusan Matematika FMIPA UNM ]]>
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DOI: 10.35580/jmathcos.v8i1.7282
<![CDATA[High inflation can threaten economic stability, with CPI as the main indicator to measure the inflation rate. Luwuk City experiences significant CPI fluctuations, reflecting economic uncertainty so an accurate forecasting method is needed. This research aims to apply Singh's Fuzzy Time Series (FTS) method optimised with Particle Swarm Optimization (PSO) to improve CPI forecasting accuracy. This research includes quantitative research, using secondary data obtained from monthly CPI data in Luwuk City on the official website of the Badan Pusat Statistik Kota Luwuk. The results showed that the use of PSO optimisation on Singh's FTS was able to optimise the prediction accuracy level of Singh's FTS forecasting on Luwuk City CPI data, with a MAPE value of 0.45%, where this value is less than 10% which indicates that the forecasting accuracy is very accurate.]]>
<![CDATA[Penerapan Model ARFIMA-LSTM Menggunakan Variasi Estimasi Parameter Pembeda Dalam Meramalkan data IHPBI ]]>
<![CDATA[Harun, Trieke Nurfadilah]]>;
<![CDATA[Djakaria, Ismail]]>;
<![CDATA[Yahya, Nisky Imansyah]]>;
<![CDATA[Nasib, Salmun K]]>;
<![CDATA[Hasan, Isran K]]>
<![CDATA[ Jurnal Riset Mahasiswa Matematika Vol 4, No 5 (2025): Jurnal Riset Mahasiswa Matematika ]]>
Publisher : <![CDATA[Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang ]]>
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DOI: 10.18860/jrmm.v4i5.33303
<![CDATA[Indeks Harga Perdagangan Besar Indonesia (IHPBI) merupakan indikator penting dalam mengukur perkembangan ekonomi, khususnya pada sektor pertanian yang memiliki pengaruh besar terhadap daya beli masyarakat. Fluktuasi harga di sektor ini berdampak langsung pada kesejahteraan konsumen dan produsen, sehingga diperlukan metode peramalan yang akurat. Penelitian ini bertujuan untuk meramalkan IHPBI sektor pertanian menggunakan pendekatan hybrid Autoregressive Fractionally Integrated Moving Average (ARFIMA) dan Long Short-Term Memory (LSTM), serta membandingkan performa metode estimasi parameter pembeda terbaik. Model ARFIMA digunakan untuk menangani komponen stasioner dan pola jangka panjang melalui diferensiasi pecahan, sedangkan LSTM digunakan untuk menangkap pola nonlinier dalam data. Keterbaruan dalam penelitian ini adalah membandingkan parameter pembeda terbaik yaitu Local Whittle dan Rescaled Range Statistics dalam hybrid ARFIMA-LSTM. Hasil dari penelitian yaitu peramalan menunjukkan tren naik IHPBI sektor pertanian selama 12 bulan ke depan. Metode estimasi parameter pembeda terbaik dalam model ARFIMA adalah Rescaled Range Statistics dengan nilai sebesar 0,322. Model hybrid ini menghasilkan nilai MAPE sebesar 0,6337853%, yang menunjukkan tingkat akurasi sangat tinggi.]]>
