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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Inferensi Jurnal Statistika dan Komputasi (STATKOM) Jurnal Pendidikan Matematika
Idrus Syahzaqi
Department Of Mathematics, Faculty Of Science And Technology, Universitas Airlangga, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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MODELING AND FORECASTING THE TOTAL VOLUME OF GOODS TRANSPORTED BY RAIL IN INDONESIA USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA) Syahzaqi, Idrus; Sediono, Sediono; Anggakusuma, Aurellia Calista; Wieldyanisa, Ezha Easyfa; Oktavia, Sabrina Salsa
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp829-842

Abstract

Transportation has an important role in supporting the mobility of people in Indonesia. Trains are included in the most widely used transportation category because they are effective and efficient, not only transporting passengers, trains also have a role in the distribution of goods. This study aims to model and forecast total volume of goods transported through rail transportation in Indonesia using the Seasonal Autoregressive Integrated Moving Average (SARIMA) Method because the data has seasonal trend. The data used comes from the Central Statistics Agency (BPS) from January 2013 to April 2024. The results were obtained that the SARIMA (0,1,1)(0,1,1)12 model is the best model with a MAPE value of 0.96% which is included in the category of accurate model. In addition to being an additional insight, this research can also be a reference in the management of railway transportation considering the number of uses both passengers, the distribution of goods that continue to increase, and can be recommendation for other research that same topic with it.
Forecasting Futures Gold Prices Using Pulse Function Intervention Analysis Approach Miranda, Ariadna Sopia; Andriani, Putu Eka; Sediono, Sediono; Syahzaqi, Idrus
Inferensi Vol 8, No 1 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i1.21979

Abstract

Gold is a precious metal that plays an important role in global trade and is often use as a financial standard in various countries. In 2024, gold prices surged sharply due to global macroeconomic factors, such as economic uncertainty, positioning gold as a safe haven for investors. Accurate predictions of future gold prices are crucial for helping investors make informed decisions and adapt to market changes. In line with Sustainable Development Goal (SDG) 8 on Decent Work and Economic Growth, this study uses the pulse function intervention analysis approach to predict gold prices by identifying patterns of changes in the pre-intervention and post-intervention periods. This study aims to make a significant contribution to the use of comprehensive and relevant predictive tools by considering the effects of interventions, supporting investor decision-making, and contributing to economic growth. The best model was obtained at ARIMA (0,2,1) with intervention parameters b=0, r=2, and s=0. The prediction results show a close alignment with actual data, yielding a MAPE value of 1.289%. Additionally, this model produces the smallest AIC value of 1125.1, an SBC value of 1135.86, and an MSE value of 1403.11, demonstrating excellent predictive capability.
PREDICTION OF AVERAGE TEMPERATURE IN BANYUWANGI REGENCY USING SARIMA Syahzaqi, Idrus; Sediono, Sediono; Dyaksa, Mega Kurnia; Vionita, Anggi Triya; Ghasani, Anisah Nabilah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp2207-2218

Abstract

Climate change due to human activity has significantly impacted increasing global average temperatures, including in Banyuwangi Regency, East Java. The impact is felt in several sectors, such as agriculture, tourism, and health. As a preventive measure to minimize the adverse effects that will occur in the future, an accurate prediction of the average temperature of Banyuwangi Regency is needed. This research used secondary data from the official website of the Central Statistics Agency (BPS) of Banyuwangi Regency per month from January 2012 to December 2023. Predictions are made using the seasonal autoregressive integrated moving average (SARIMA) approach. The best model is selected based on its fulfillment of stationarity, the significance of its parameters, and compliance with the assumptions of normality and white noise. From this method, the best model obtained to predict the average temperature of Banyuwangi Regency is the probabilistic SARIMA (1,0,0)(0,1,1)12. The probabilistic SARIMA model treats both parameters and forecasts as probability distributions. The average temperature of Banyuwangi Regency is obtained for the next year, namely from January 2023 to December 2023, with a MAPE of 1.63%. With an accuracy rate of 98.37%, it can be said that the probabilistic SARIMA (1,0,0)(0,1,1)12 model is accurate in predicting the average temperature of Banyuwangi Regency in the future. Thus, the prediction of the average temperature of Banyuwangi Regency is expected to help the community and government manage the impact of erratic climate change to improve the welfare of all Banyuwangi people.
Peramalan Jumlah Barang Kereta Api di Indonesia Menggunakan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) Syahzaqi, Idrus; Sediono, Sediono; Oktavia, Sabrina Salsa; Anggakusuma, Aurellia Calista; Wieldyanisa, Ezha Easyfa
Jurnal Statistika dan Komputasi Vol. 4 No. 1 (2025): Jurnal Statistika dan Komputasi
Publisher : Universitas Nahdlatul Ulama Sunan Giri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/statkom.v4i1.4424

Abstract

Background: Freight transportation is an important part of the business run by PT Kereta Api Indonesia. To support effective strategic planning and infrastructure development, an accurate prediction of the amount of goods to be transported in the future is required. Therefore, historical data-based forecasting methods such as Seasonal Autoregressive Interated Moving Average (SARIMA) can be a relevant approach to predict the number of railway goods in Indonesia. Objective: Obtain a suitable model to forecast the number of goods transported by rail transportation in Indonesia, and to determine the results of the forecasting. Methods: This research uses the time series method with the Seasonal Autoregressive Integrated Moving Averang (SARIMA) model approach based on data characteristics that show seasonal patterns. SARIMA itself is able to integrate seasonal pattern components in the data and is able to effectively capture periodic and structural dynamics in seasonal data. Results: The best model obtained is probabilistic SARIMA(0,1,1)(0,1,1)12, using secondary data sourced from the Central Bureau of Statistics (BPS) in the range of January 2013 to March 2024. Forecasting for the next 12 months (April 2023 to March 2024) shows a Mean Absolute Percentage Error (MAPE) value of 8.03% which indicates that the level of forecasting accuracy is very good. Conclusion: The probabilistic ARIMA(0,1,1)(0,1,1)12 model can be used as a reliable reference in predicting the amount of goods transported through rail transportation in Indonesia.
SELECTING OPTIMAL PROCESS PARAMETERS OF Al2O3/C COMPOSITE USING GRA WITH PCA AND TAGUCHI’S QLF APPROACH Syahzaqi, Idrus; Rochmanto, Hani Brilianti; Ahsan, Muhammad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (549.889 KB) | DOI: 10.30598/barekengvol16iss3pp1039-1050

Abstract

The aim of this study is to find the controlled factors affecting the mass density of the combined Al2O3/Cu. All experiments were carried out using powder metallurgy. Experiments were carried out with four controllable powder processing parameters, namely milling time, compaction pressure, sintering temperature, and holding time. The L18 mixed-level Taguchi Orthogonal Array was used for experimental because it is the basis for the analysis of the Taguchi method. In this research, statistical analysis is carried out using GRA with PCA and Quality Loss Function. The result was the best model based on the Quality Loss Function, because the method has the biggest determination coefficient value is 99,97% where the results is better than GRA with PCA. From the main effect table study, the optimal combination of parameters for response: mass density and hardness are A2B3C3D2 powder metallurgical process parameters, namely milling time of 360 minutes, compacting powder of 200 MPa, sintering of 7000C, and holding time of 20 minutes. The ANOVA results show that the compaction pressure has the most influential parameter that affects the response. The percentage contribution of compaction pressure is 87.09%. Based on ANOVA, the R-squared value is 99.97%, which means the tested factor variables can explain the density of the Al2O3/Cu composite by 99.70%. Therefore, only 18 experimental trials are needed to discover the reality of what will happen in the process.
MODELLING OF POVERTY PERCENTAGE IN EAST JAVA PROVINCE WITH SEMIPARAMETRIC REGRESSION APPROACH Syahzaqi, Idrus; A., Salman Alfarizi P.; Fithriasari, Kartika
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp0727-0734

Abstract

Poverty is an economic problem faced by all countries in the world, including Indonesia. Poverty is seen as the inability of a person from an economic standpoint to meet basic food and non-food needs as measured from the expenditure side. East Java Province is used as the object of research because this province has the highest economic growth in Java Island after DKI Jakarta province in the last 5 years. However, East Java is also included in the province with the highest number of poor people on the island of Java. Several independent variables that are thought to influence the percentage of poverty in East Java are the Open Unemployment Rate (TPT), Life Expectancy Rate (AHH), Average Years of Schooling (RLS), Population Density, and GRDP Rate. Sources of research data come from the East Java BPS website and East Java Open Data. Data analysis was performed using a semiparametric regression approach. The results of the analysis obtained good performance values, namely the MSE value of 12,2156 and the R2 value of 98,71%.
GROUPING PROVINCES IN INDONESIA BASED ON THE NUMBER OF VILLAGES AFFECTED BY ENVIROMENTAL POLLUTION WITH K-MEDOIDS, FUZZY C-MEANS, AND DBSCAN Syahzaqi, Idrus; Effendi, Magdalena; Rahmawati, Hasri; Kuswanto, Heri; Sediono, Sediono
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp0923-0936

Abstract

Pollution can cause the environment to not function properly and ultimately harm humans and other living things. Environmental pollution is a problem that needs to be resolved because it involves the safety, health, and survival of living things. Air pollution in Pekanbaru due to a long dry season has resulted in forest fires. Then, 70% of drinking water is contaminated by fecal waste. In addition, the contamination of the land by the Chevron company resulted in residents suing the company. Until now, there has been no research that has carried out a comparison between methods for grouping villages affected by environmental pollution at the provincial level in Indonesia, so it is important to select the best method for carrying out the grouping. The limitations of this research are the use of three methods for clustering: K-Medoids, Fuzzy C-Means, and DBSCAN. The results showed that Fuzzy C-Means with five clusters have an optimal value compared to DBSCAN with an ICD rate value of 0,351. This method can be used by the government to improve the quality of villages that are clean from pollution in Indonesia, monitoring and evaluation based on the clusters formed.
Survival Time Analysis of Multiple Myeloma Patients using Type 1 Censored Exponential Distribution Parameter Estimation Wahyu Subekti, Cahya Arsyika; Nilasari, Inas; Syarif, Devi Mufidah; Syahzaqi, Idrus; Kurniawan, Ardi
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.32179

Abstract

Multiple myeloma is a type of blood cancer that attacks plasma cells in the bone marrow and affects the immune system. This study analyzes the survival time of patients with multiple myeloma using Type 1 censored exponential distributed parameter estimation. The data, consisting of 47 patients (35 uncensored and 12 censored), were tested for exponential distribution fit using the Anderson-Darling test, yielding a p-value of 0.495, confirming the suitability of the exponential model. The maximum likelihood estimation method was applied, resulting in a parameter estimate (θ ̂) of approximately 54.028 days, representing the mean survival time. Hypothesis testing and confidence intervals were conducted, with the 95% confidence interval for θ_0 ranging between 32 and 53 days. The findings suggest that the exponential distribution effectively models the survival data, providing insights into patient survival trends and supporting clinical decision-making.
Monitoring PH of Shrimp Water using Progressive Max Chart Rosyadi, Niam; Syahzaqi, Idrus; Ibrahim, Auron Saka; Sihotang, Raja Van Den Bosch; Ahsan, Muhammad; Mashuri, Muhammad
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 4 (2025): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i4.30255

Abstract

Control charts aim to reduce variability in the process and monitor for out-of- control processes. So far, the process of monitoring quality is usually carried out partially, namely monitoring the mean process and process variability. This approach is less effective and time-consuming because two separate charts must be created simultaneously. One alternative is to analyze both parameters simultaneously, such as through the Progressive Max Chart method (Mixed-Methods Research: Quantitative and Applied). The Progressive Max Chart is a control chart designed for monitoring both the mean and variability by considering the case of subgroup observations. This study uses a quantitative approach, combining primary data collection and simulations to generate findings through statistical analysis and quantifiable measurements. The purpose of this research is to compare methods such as the Progressive Max Chart, EWMA-Max, and Max Chart. The analysis results show that the Progressive Max Chart method performs better than the Max Chart and EWMA- Max Chart, both in terms of mean, variance, and mean-variance detection, for small shifts and large shifts. The control chart performance results provide optimal outcomes for monitoring out-of-control signals at subgroup sizes of n = 2, 3, 5. This is characterized by ARL₁ values that approach 1 more quickly. This method is applied to pH data from vannamei shrimp pond water located in Madura. The Progressive Max Chart method provides optimal results by maximizing the detection of in-control signals. Additionally, it is tested on synthesized data and demonstrates optimal performance in detecting both small and large shifts in mean, variance, and mean-variance.
Peran Mahasiswa Pada Program Asistensi Mengajar: Analisis Pemahaman Siswa Kelas 11 Pada Mata Pelajaran Matematika dengan Menggunakan Uji Kruskal-Wallis dan Uji Mann-Whitney Al Hasri, Ilham; Syahzaqi, Idrus
Jurnal Pendidikan Matematika Vol. 3 No. 1 (2025): November
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ppm.v3i1.2199

Abstract

Kegiatan Asistensi Mengajar merupakan bagian dari MBKM yang bertujuan untuk meningkatkan kualitas pendidikan di satuan pendidikan yang dilakukan oleh mahasiswa serta evaluasi hasil pembelajaran dengan tujuan penyusunan strategi pembelajaran berdasarkan tingkat pemahaman siswa. Program Asistensi Mengajar dilakukan pada SMAN 1 Driyorejo dengan beberapa tahapan, yaitu tahap persiapan, pelaksanaan, dan evaluasi. Tahap persiapan meliputi penentuan sekolah mitra dan pengenalan lingkungan sekolah. Tahap pelaksanaan meliputi kegiatan mengajar, non mengajar, dan adminitrasi sekolah. Kegiatan mengajar dilakukan pada ruang kelas dengan pemaparan materi, latihan soal, dan sesi diskusi. Berikutnya, kegiatan non mengajar adalah kegiatan siswa di luar kelas pada lingkup sekolah, seperti upacara dan lomba menyambut hari kemerdekaan. Selanjutnya, kegiatan administratif yang dilakukan adalah melakukan rekap absensi hingga nilai ujian siswa. Tahap evaluasi dilakukan dengan melakukan analisis pada hasil ujian matematika dengan pendekatan nonparametrik dengan menggunakan uji Kruskal-Wallis dan uji Mann-Whitney. Berdasarkan hasil yang diperoleh dari uji Kruskal-Wallis, terdapat perbedaan signifikan pada hasil ujian matematika antar kelas sehingga dilakukan uji lanjutan dengan uji Mann-Whitney. Dari hasil evaluasi, diperlukan beberapa strategi agar materi dapat diterima oleh siswa secara optimal, seperti menyesuaikan tingkat kesulitan materi hingga metode pembelajaran berbasis diskusi.
Co-Authors Ahsan, Muhammad Al Hasri, Ilham Andriani, Putu Eka Anggakusuma, Aurellia Calista Ardi Kurniawan Az Zuhro, Muhammad Rosyid Ridho Dita Amelia, Dita Dyaksa, Mega Kurnia Effendi, Magdalena Fithriasari, Kartika Ghasani, Anisah Nabilah Gunawan, Syifa Hani Brilianti Rochmanto Heri Kuswanto Ibrahim, Auron Saka Miranda, Ariadna Sopia muhammad mashuri Nilasari, Inas Oktavia, Sabrina Salsa Rahmawati, Hasri Rosyadi, Niam Salman Alfarizi Pradana Andikaputra Sediono, Sediono Sihotang, Raja Van Den Bosch Syarif, Devi Mufidah Vionita, Anggi Triya Wahyu Subekti, Cahya Arsyika Wieldyanisa, Ezha Easyfa