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All Journal Statistika Pharmaciana: Jurnal Kefarmasian EKOMBIS REVIEW: Jurnal Ilmiah Ekonomi dan Bisnis EKSPANSI Amwaluna Jurnal Ekonomi dan Keuangan Syariah TRANSFORMASI : JURNAL PENGABDIAN PADA MASYARAKAT Indonesian Journal of Economics and Management Jurnal Riset Statistika Bandung Conference Series: Statistics
Teti Sofia Yanti
Program Studi Statistik, Universitas Islam Bandung
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Industrial & Manufacturing Engineering Mathematics Medicine & Pharmacology Social Sciences Other

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Identifikasi Sektor Unggulan Enam Provinsi di Pulau Jawa Melalui Analisis Input Output terhadap Tabel IRIO Fariz Andinur Aziz; Teti Sofia Yanti
Bandung Conference Series: Statistics Vol. 4 No. 2 (2024): Bandung Conference Series: Statistics
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v4i2.13578

Abstract

Abstract. Input output analysis provides information on transactions of goods and services between economic sectors in a region. Due to differences in resources between regions, an Interregional Input-Output (IRIO) Table is needed to explain economic linkages between regions more comprehensively. This table is very important to understand the economic performance of various regions by looking at the linkages between sectors and regions, both in terms of backward and forward linkages. However, just knowing the linkages is not enough to develop the economy of a region. Therefore, further analysis is needed to identify leading sectors that have the potential to drive economic growth. Java Island is known as the main center of economic growth in Indonesia. This study aims to determine the inter-sectoral and regional linkages of six provinces in Java Island, and identify the leading sectors in each province using the IRIO Table. Based on the linkages in DKI Jakarta, sectors 4 (electricity and gas procurement) and 11 (corporate services) have the highest backward and forward linkages. In West Java, Central Java, and East Java, sectors 3 (manufacturing industry) and 4 (electricity and gas procurement) are the highest. In DI Yogyakarta, sectors 4 (electricity and gas procurement) and 8 (transportation and storage) are highest, and in Banten, sector 4 (electricity and gas procurement) is highest. By leading sector, sector 3 (manufacturing industry) is leading in all provinces except Banten, sector 4 (electricity and gas procurement) in all provinces, sector 8 (transportation and storage) in West Java, DI Yogyakarta, and East Java, and sector 10 (information and communication) in DI Yogyakarta and East Java. Abstrak. Analisis input output memberikan informasi tentang transaksi barang dan jasa antar sektor ekonomi di suatu wilayah. Karena perbedaan sumber daya antar wilayah, diperlukan Tabel Interregional Input-Output (IRIO) untuk menjelaskan keterkaitan ekonomi antar wilayah secara lebih komprehensif. Tabel ini sangat penting untuk memahami performa ekonomi berbagai wilayah dengan melihat keterkaitan antar sektor dan wilayah, baik dari sisi keterkaitan ke belakang (backward linkage) maupun ke depan (forward linkage). Namun, hanya mengetahui keterkaitan saja tidak cukup untuk mengembangkan perekonomian suatu wilayah. Oleh karena itu, perlu dilakukan analisis lebih lanjut untuk mengidentifikasi sektor-sektor unggulan yang berpotensi mendorong pertumbuhan ekonomi. Pulau Jawa dikenal sebagai pusat pertumbuhan ekonomi utama di Indonesia. Penelitian ini bertujuan untuk mengetahui keterkaitan antar sektor dan wilayah enam provinsi di Pulau Jawa, serta mengidentifikasi sektor unggulan di masing-masing provinsi tersebut menggunakan Tabel IRIO. Berdasarkan keterkaitan di DKI Jakarta, sektor 4 (pengadaan listrik dan gas) dan 11 (jasa perusahaan) memiliki keterkaitan ke belakang dan ke depan paling tinggi. Di Jawa Barat, Jawa Tengah, dan Jawa Timur, sektor 3 (industri pengolahan) dan 4 (pengadaan listrik dan gas) paling tinggi. Di DI Yogyakarta, sektor 4 (pengadaan listrik dan gas) dan 8 (transportasi dan pergudangan) paling tinggi, dan di Banten, sektor 4 (pengadaan listrik dan gas) paling tinggi. Berdasarkan sektor unggulan, sektor 3 (industri pengolahan) unggul di semua provinsi kecuali Banten, sektor 4 (pengadaan listrik dan gas) di semua provinsi, sektor 8 (transportasi dan pergudangan) di Jawa Barat, DI Yogyakarta, dan Jawa Timur, serta sektor 10 (informasi dan komunikasi) di DI Yogyakarta dan Jawa Timur.
Analisis Regresi Tobit pada Jumlah Kasus Kematian HIV AIDS di Provinsi Jawa Barat Fiore Rosie Kestana; Teti Sofia Yanti
Bandung Conference Series: Statistics Vol. 4 No. 2 (2024): Bandung Conference Series: Statistics
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v4i2.15262

Abstract

Abstract. There are several types of regression analysis, one of which is linear regression. In fact, in the field there are many cases where the dependent variable doesn’t complete information or is called cencored data. Regression takes account censored data on the dependent variable is tobit regression. Parameter estimation in tobit regression uses the Maximum Likelihood Estimation method. This research takes data related to cases of death due to HIV/AIDS. In June 2022, West Java in third place with the highest of HIV/AIDS cases in Indonesia. The number of cases of AIDS death in Indoneisa, one of which in West Java, the probability of this occurring is 0 to not death. This data is called censored data. This research aims was to look at the factors that influence the number of AIDS death in West Java with the the variables X of the number of cases of HIV positive blood donor X1 , the number of cases of syphilis X2 and the number of condom X3 using a significance level of 5%. Based on the results, it is stated that for every people 10 people increasing the number of cases of syphilis, the number of deaths due to AIDS will increase by 5 people and every time the number of condum uses increases by 10^1000, the number of deaths due to AIDS will decrease by 1,2 cases. The variables that influence the number of deaths due to AIDS are the number of cases of syphilis X2 of the number of condom Log X3. Abstrak. Terdapat beberapa macam analisis regresi salah satunya yaitu analisis regresi linier. Faktanya, dilapangan banyak ditemukan kasus dimana variabel terikatnya tidak memberikan informasi secara lengkap atau disebut data tersensor. Regresi yang memperhatikan data tersensor pada variabel terikatnya yaitu analisis regresi tobit. Metode ini digunakan untuk mengatasi ketika variabel terikatnya bersifat terbatas. Pendugaan parameter pada regresi tobit yaitu menggunakan metode Maksimum Likelihood Estimation. Penelitian ini mengambil data terkait kasus kematian oleh HIV/AIDS. Provinsi Jawa Barat sampai dengan Juni 2022 terdapat pada urutan ketiga dengan jumlah kasus HIV/AIDS terbanyak di Indonesia. Virus HIV/AIDS merupakan salah satu penyakit dengan peringkat atas dalam menyebabkan kematian. Dalam jumlah kasus kematian AIDS di Indonesia salah satunya di Jawa Barat kemungkinan yang terjadi adalah 0 sampai tak hingga kematian. Struktur data seperti ini disebut data tersensor. Pada penelitian ini bertujuan untuk melihat faktor-faktor yang mempengaruhi pada jumlah kematian AIDS di Jawa Barat dengan variabel X nya terdiri dari jumlah kasus donor darah positif HIV X , jumlah kasus penyakit sifilis X2 dan jumlah penggunaan kondom X3 dengan menggunakan taraf nyata 5%. Berdasarkan hasil penelitian menyatakan bahwa setiap penambahan jumlah kasus penyakit sifilis 10 orang maka jumlah kematian akibat AIDS akan meningkat sebesar 5 orang dan setiap jumlah penggunaan kondom bertambah 10^1000 maka jumlah kasus kematian akibat AIDS akan menurun sebesar 1,2 kasus. Selain itu, berdasarkan hasil penelitian ini bahwa variabel yang mempengaruhi jumlah kematian akibat AIDS adalah jumlah kasus penyakit sifilis X2 dan logaritma jumlah penggunaan kondom Log X3.
MSME Financing in Islamic Banks and Poverty in Indonesia Setiawan, Iwan; Tripuspitorini, Fifi Afiyanti; Ruhana, Nafisah; Yanti, Teti Sofia
Indonesian Journal of Economics and Management Vol. 4 No. 1 (2023): Indonesian Journal of Economics and Management (November 2023)
Publisher : Jurusan Akuntansi Politeknik Negeri Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35313/ijem.v4i1.5054

Abstract

Poverty is a socio-economic problem faced by all nations. The poverty rate has decreased, but the number and percentage are still quite high. One solution to reduce poverty is to increase the income of Micro, Small and Medium Enterprises (MSMEs). MSMEs involve the majority of business actors and the lower middle class, the success of the MSME sector will reduce poverty. The main obstacle in the development of MSMEs is the lack of funds and limited access to funding from financial institutions. Islamic banks play a role in overcoming poverty through financing activities that are directly provided to MSMEs. Sharia Bank financing for MSMEs is still limited, trending downwards and does not meet the minimum requirements of Bank Indonesia. This research was conducted to examine the role of sharia bank MSME financing for poverty in Indonesia. This study uses qualitative and quantitative methods using a simultaneous regression analysis model, so that the causes and interrelationships between variables can be stated clearly. The results of this study reveal that Islamic bank financing for MSMEs plays a role in poverty alleviation indirectly through economic growth. Poverty is directly affected by the capital and quality of financing in Islamic banks. The central bank's interest rate policy and inequality in income distribution have contributed to poverty in Indonesia. Efforts to reduce poverty can be carried out by increasing the quality of financing in the MSME sector, encouraging economic growth, setting optimal policy interest rates and reducing inequality in income distribution in society.
The Role of Islamic Bank MSME Financing for Job Creation in Indonesia Setiawan, Iwan; Tripuspitorini, Fifi Afiyanti; Ruhana, Nafisah; Yanti, Teti Sofia
Indonesian Journal of Economics and Management Vol 4 No 3 (2024): Indonesian Journal of Economics and Management (July 2024)
Publisher : Jurusan Akuntansi Politeknik Negeri Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35313/ijem.v4i3.6175

Abstract

Population growth has an impact on increasing unemployment due to the limited job opportunities available in Indonesia. The solution to overcoming unemployment is carried out through the development of labor-intensive economic sectors. The micro, small and medium enterprise (MSME) sector has the potential to create jobs. However, the development of MSMEs is hampered by limited sources of funds that can be accessed from financial institutions (banks). The Islamic bank financing system is in accordance with the characteristics of MSMEs, has the potential to encourage increased business and employment for MSMEs. This research aims to examine the impact of Islamic bank financing for MSMEs on job creation in Indonesia. The research was carried out using a multiple regression analysis model with the Ordinary Least Square (OLS) estimation method, using time series data for the monthly period 2016-2023. The research results show that Islamic bank financing in the MSME sector has a significant positive effect on job creation. Every increase in sharia bank financing in the MSME sector provides a direct contribution to job creation. Job creation is also significantly influenced by third party funds from Islamic banks, monetary policy, minimum wage policy, unemployment and economic growth. Increases in third party funds, minimum wages and economic growth have a significant effect on job creation. Increasing monetary policy interest rates and unemployment result in a decrease in job creation.
Pemodelan Intervensi untuk Meramalkan Jumlah Penumpang Pesawat Domestik Bandara Internasional Sultan Hasanuddin Makassar Salma, Ghina; Yanti, Teti Sofia
Jurnal Riset Statistika Volume 4, No. 1, Juli 2024, Jurnal Riset Statistika (JRS)
Publisher : UPT Publikasi Ilmiah Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jrs.v4i1.3892

Abstract

Abstract. SARIMA is a time series method designed for data with seasonal components, assuming stationarity. When data is non-stationary due to shocks, it may involve interventions. In March 2020, the first case of Covid-19 was detected in Indonesia, causing data to contain interventions due to a sharp decline in data patterns. Intervention events occurred because the Indonesian government implemented the Large-Scale Social Restrictions policy to control the virus's spread and protect public health, involving various aspects, including restricting air transportation. The purpose of this research is to determine the appropriate model between SARIMA and Intervention, evaluated based on the Akaike Information Criterion (AIC). The data used is the actual number of domestic flight passengers departing from Sultan Hasanuddin Airport. From the testing results of both methods, the SARIMA model obtained is ARIMA (1, 1, 0)(0, 1, 0)12 with an AIC value of 1.673, while the Intervention model is ARIMA (1, 1, 0)(0, 1, 0)12, b = 0, s = 5, and r = 1 with an AIC value of 1.662. Thus, the intervention method is better suited for forecasting the number of domestic flight passengers at Sultan Hasanuddin Airport due to its lower AIC value. Abstrak. SARIMA adalah data deret waktu yang mengandung unsur musiman dengan asumsi yang harus dipenuhi yaitu asumsi stasioneritas. Ketika data tidak stasioner karena adanya shock pada data, maka data tersebut mengandung intervensi. Pada bulan Maret 2020, kasus Covid-19 pertama kali terdeteksi di Indonesia yang menyebabkan data mengandung intervensi karena pola data mengalami penurunan yang sangat tajam. Kejadian intervensi terjadi karena Pemerintah Indonesia mengeluarkan kebijakan terhadap PPKM yang diterapkan untuk mengendalikan penyebaran virus dan melindungi kesehatan masyarakat, yang melibatkan berbagai aspek salah satunya membatasi perjalanan transportasi udara. Tujuan penelitian ini adalah mengetahui model yang tepat antara SARIMA dan Intervensi yang dihitung berdasarkan Akaike Information Criterion (AIC). Data yang digunakan yaitu data aktual jumlah penumpang pesawat domestik yang berangkat dari Bandara Sultan Hasanuddin. Dari hasil pengujian kedua metode, diperoleh model SARIMA yaitu ARIMA (1, 1, 0)(0, 1, 0)12 dengan nilai AIC sebesar 1.673, sedangkan model intervensi yaitu ARIMA (1, 1, 0)(0, 1, 0)12, b = 0, s = 5, dan r = 1 dengan nilai AIC sebesar 1.662. Sehingga metode intervensi lebih baik digunakan untuk meramalkan jumlah penumpang pesawat domestik di Bandara Sultan Hasanuddin karena memiliki nilai AIC yang lebih kecil.
Diagram Kendali Adaptive Exponentially Weighted Moving Average Bayesian dalam Pengendalian Penyaluran Air Minum Muhammad Farhan Praja Utama; Teti Sofia Yanti
Jurnal Riset Statistika Volume 4, No. 2, Desember 2024, Jurnal Riset Statistika (JRS)
Publisher : UPT Publikasi Ilmiah Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jrs.v4i2.5014

Abstract

Abstract. Process control is one of the statistical methods in maintaining product quality. One of the process control methods used in statistical quality control is a control diagram, which is a diagram used to describe the condition of an observation in a certain period of time, whose observation value is limited by the upper control limit (BKA) and lower control limit (BKB) to control the observation. Drinking water is one of the main needs needed by the community. In an effort to fulfill the community's need for drinking water availability, appropriate steps are needed in an effort to control the drinking water distribution process at drinking water companies so that the distribution process becomes more effective. This research was conducted to see the drinking water distribution process at PT.X as a step to control the distribution process. Researchers use the Adaptive Exponentially Weighted Moving Average (AEWMA) control chart by utilizing the Bayesian theorem so that the threshold value of the Bayes confidence interval derived from the posterior distribution is obtained. The results obtained that the AEWMA control chart is quite effective in providing an overview of the movement of the process by showing values that are in control and values that are out of control. Abstrak. Pengendalian proses merupakan salah satu metode statistik dalam menjaga kualitas produk. Salah satu metode pengendalian proses yang digunakan dalam pengendalian kualitas statistik adalah diagram kendali, yaitu diagram yang digunakan untuk menggambarkan kondisi suatu pengamatan dalam periode waktu tertentu, yang nilai pengamatannya dibatasi oleh batas kendali atas (BKA) dan batas kendali bawah (BKB) untuk mengendalikan pengamatan. Air minum merupakan salah satu kebutuhan utama yang dibutuhkan oleh masyarakat. Dalam upaya pemenuhan kebutuhan masyarakat akan ketersediaan air minum diperlukan langkah yang tepat dalam upaya pengendalian proses penyaluran air minum pada perusahaan air minum agar proses penyaluran menjadi lebih efektif. Penelitian ini dilakukan untuk melihat proses penyaluran air minum di PT.X sebagai langkah pengendalian proses penyalurannya tersebut. Peneliti menggunakan diagram kendali Adaptive Exponentially Weighted Moving Average (AEWMA) dengan memanfaatkan teorema bayesian sehingga diperoleh nilai threshold dari interval kepercayaan bayes yang berasal dari distribusi posterior. Adapun hasil yang diperoleh bahwa diagram kendali AEWMA cukup efektif dalam memberikan gambaran pergerakan proses dengan menunjukan nilai-nilai yang berada dalam kendali dan nilai yang out of control.
Peramalan Jumlah Kunjungan Pasien Balita dengan Metode Holt’s Double Exponential Smoothing Aida Nurul Islah; Teti Sofia Yanti
Jurnal Riset Statistika Volume 4, No. 2, Desember 2024, Jurnal Riset Statistika (JRS)
Publisher : UPT Publikasi Ilmiah Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jrs.v4i2.5026

Abstract

Abstract. The Community Health Center is one of the first-level health facilities whose role is to provide health services to the community. Integrated Managemen Terpadu Balita Sakit (MTBS) is an integrated approach that focuses on child health. MTBS aims to reduce illness and disability, reduce preventable mortality, and support healthy growth and development in children under five (children <5 years of age). The number of patient visits fluctuates and is difficult to predict accurately, so forecasting is necessary. Forecasting is the process of estimating future conditions by taking into account past data. In this study, Holt's Double Exponential Smoothing method was used. Holt's Double Exponential Smoothing method is used for data that has a trend where the calculation uses two smoothing constants, namely α (level) and γ (trend). In this study, the concept of trial and error is used in determining the best constant. The data used is data on monthly patient visits at the MTBS clinic at the Margahayu Raya Health Center from January 2021 to August 2023, totaling 32 data. The results of this study obtained the smallest forecasting accuracy value when α = 0.9 and γ = 0.1 with a MAPE value = 23.82%. The results of forecasting for the next four periods show a positive trend, which means that the number of MTBS poly patient visits is increasing. Abstrak. Pusat Kesehatan Masyarakat (Puskesmas) merupakan salah satu fasilitas kesehatan tingkat pertama yang berperan untuk memberikan pelayanan kesehatan pada masyarakat. Managemen Terpadu Balita Sakit (MTBS) yaitu pendekatan terpadu yang berfokus pada Kesehatan anak. MTBS bertujuan untuk mengurangi penyakit dan kecacatan, menurunkan angka kematian yang dapat dicegah, serta mendukung pertumbuhan dan perkembangan sehat pada anak balita (anak usia < 5 tahun). Jumlah kunjungan pasien yang berfluktuasi dan sulit diprediksi secara akurat, sehingga peramalan diperlukan. Peramalan atau forecasting merupakan proses memperkirakan keadaan di masa yang akan datang dengan memperhitungkan data masa lalu. Dalam penelitian ini digunakan metode Holt’s Double Exponential Smoothing. Metode Holt Double Exponential Smoothing ini digunakan untuk data yang memiliki tren dimana dalam perhitungannnya menggunakan dua konstanta pemulusan yaitu α (level) dan γ (tren). Dalam penelitian ini digunakan konsep trial and error dalam menentukan konstanta terbaik. Data yang digunakan yaitu data kunjungan pasien bulanan pada poli MTBS di Puskesmas Margahayu Raya dari bulan Januari 2021 hingga Agustus 2023 sebanyak 32 data. Hasil dari penelitian ini diperoleh nilai akurasi peramalan yang terkecil pada saat α = 0,9 dan γ = 0,1 dengan nilai MAPE = 23,82%. Hasil dari peramalan untuk empat periode kedepan menunjukan adanya tren positif yang berarti bahwa jumlah kunjungan pasien poli MTBS mengalami kenaikan.
Improving Regression Model Menggunakan Bagging MARS Terhadap Gini Ratio di Pulau Jawa Diana Erviana; Teti Sofia Yanti
Jurnal Riset Statistika Volume 5, No. 1, Juli 2025, Jurnal Riset Statistika (JRS)
Publisher : UPT Publikasi Ilmiah Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jrs.v5i1.6488

Abstract

Abstract. Economic inequality in Java Island is a crucial issue that impacts sustainable development. This study aims to model the Gini ratio using the Multivariate Adaptive Regression Splines (MARS) method combined with Bootstrap Aggregating (Bagging). Secondary data includes the response variable Gini ratio and predictor variables such as GRDP, Open Unemployment Rate, Percentage of Poor Population, Population Growth, and Labor Force Participation Rate. The determination of the number of bootstrap replications (B = 10, 25, 50, 100, and 200), as well as model parameter settings for the maximum number of basis functions, maximum interaction, and minimum observation, were conducted. The best model was selected based on the replication with the lowest average Generalized Cross Validation (GCV) value and the highest R-squared value. The analysis results showed that the 100th replication produced the lowest average GCV, while the final model was derived from the 41st replication, which had the highest R-squared value. The final model consists of 17 basis functions, with the most influential variable being the percentage of the poor population. Based on the findings, it is crucial for the government to focus on reducing the percentage of the poor population through strategic policies to mitigate economic inequality in Java Island. Abstrak. Ketimpangan ekonomi di Pulau Jawa merupakan isu penting yang berdampak pada pembangunan berkelanjutan. Penelitian ini bertujuan untuk memodelkan gini ratio menggunakan metode Multivariate Adaptive Regression Splines (MARS) yang dikombinasikan dengan Bootstrap Aggregating (Bagging). Data sekunder yang digunakan mencakup variabel respon gini ratio serta variabel prediktor seperti PDRB, Tingkat Pengangguran Terbuka, Persentase Penduduk Miskin, Pertumbuhan Penduduk, dan Tingkat Partisipasi Angkatan Kerja. Penentuan jumlah replikasi bootstrap (B = 10, 25, 50, 100, dan 200), serta pengaturan parameter model juga dilakukan untuk jumlah maksimal basis fungsi, maksimum interaksi, dan minimum observasi. Model terbaik dipilih berdasarkan replikasi dengan nilai rata-rata Generalized Cross Validation (GCV) terendah dan nilai R-squared tertinggi. Hasil analisis menunjukkan bahwa replikasi ke-100 menghasilkan rata-rata GCV paling kecil, sementara model akhir yang diperoleh adalah dari replikasi ke-41 yang memiliki nilai R-squared tertinggi. Model akhir terdiri dari 17 basis fungsi, dengan variabel paling berpengaruh adalah persentase penduduk miskin. Berdasarkan hasil penelitian, penting bagi pemerintah untuk fokus pada pengurangan persentase penduduk miskin melalui kebijakan strategis guna mengurangi ketimpangan ekonomi di Pulau Jawa.
Penerapan Metode Hybrid Fuzzy Time Series pada Data IHSG 10060118082, Rizky Fauzi; Teti Sofia Yanti
Bandung Conference Series: Statistics Vol. 5 No. 2 (2025): Bandung Conference Series: Statistics
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v5i2.21452

Abstract

Abstract. Forecasting the Indonesia Composite Stock Price Index (IHSG) plays a crucial role in investment decision-making; however, the volatility and uncertainty of the data make the process complex. This study aims to apply a hybrid Fuzzy Time Series (FTS) method combined with Fuzzy C-Means (FCM) and Markov Chain algorithms to accurately model and forecast IHSG values. The data used consists of daily IHSG closing prices from January 2020 to July 2025, obtained from idx.co.id. The research steps include data preprocessing, determining the universe of discourse, identifying the optimal number of clusters using the Elbow method, constructing fuzzy intervals based on FCM cluster centers, followed by fuzzification, forming Fuzzy Logical Relationship Groups (FLRG), building the Markov transition probability matrix, and conducting prediction with result adjustments. The model was evaluated using RMSE, MAPE, and Theil’s U metrics to measure accuracy. The results showed that the hybrid method produced predictions with an RMSE of 65.8147, MAPE of 0.80%, and Theil’s U of 0.0102 after adjustment, indicating excellent model performance. In conclusion, the FTS-FCM-Markov hybrid method effectively handles the dynamic characteristics of IHSG data and can serve as an alternative approach for stock market forecasting. Abstrak. Peramalan Indeks Harga Saham Gabungan (IHSG) memiliki peran penting dalam pengambilan keputusan investasi, namun fluktuasi dan ketidakpastian data membuat proses ini menjadi kompleks. Penelitian ini bertujuan untuk menerapkan metode hybrid Fuzzy Time Series (FTS) yang dikombinasikan dengan algoritma Fuzzy C-Means (FCM) dan Markov Chain untuk memodelkan dan meramalkan nilai IHSG secara akurat. Data yang digunakan merupakan harga penutupan IHSG harian dari Januari 2020 hingga Juli 2025 yang diambil dari Idx.co.id. Langkah penelitian dimulai dengan pra-pemrosesan data, penentuan universe of discourse, penentuan jumlah klaster optimal dengan metode Elbow, pembentukan interval fuzzy berdasarkan pusat klaster FCM, kemudian dilakukan proses fuzzifikasi, pembentukan Fuzzy Logical Relationship Group (FLRG), pembangunan matriks probabilitas transisi Markov, hingga tahap prediksi dan penyesuaian hasil. Model dievaluasi menggunakan metrik RMSE, MAPE, dan Theil’s U untuk mengukur tingkat akurasi. Hasil menunjukkan bahwa metode hybrid ini mampu memberikan prediksi dengan RMSE sebesar 65.8147, MAPE 0.80%, dan Theil’s U sebesar 0.0102 setelah dilakukan penyesuaian, yang mengindikasikan performa model yang sangat baik. Kesimpulannya, metode hybrid FTS-FCM-Markov terbukti mampu menangani karakteristik data IHSG yang bersifat dinamis dan dapat dijadikan sebagai pendekatan alternatif dalam peramalan pasar saham..
Co-Authors 10060118082, Rizky Fauzi Adeliya Sanya Lenita Aida Nurul Islah Alfiari Firdaus Andi Setia Nugraha Annisa Lisa Nurjanah Ari Budi Utomo Dadang Hermawan Diana Erviana Dwi Syah Fitra Ramadhan Enu Ainun Isni Eti Kurniati Fariz Andinur Aziz Fifi Afiyanti Fiore Rosie Kestana Fitrianti Darusman Fuji Astuti Hilda Aprilia, Hilda Icih Sukarsih Iwan Setiawan Lathifah Lisnur Wachidah Mega Anisa Rachim Mey Damayanti CR Muhammad Farhan Praja Utama Nety Kurniaty Nusar Hajarisman Onoy Rohaeni Puthri Rahma Zain Putriyanto, Jasmine Salsabila Raihani Himati Kamilah Ratih Nurfitri Ruhana, Nafisah Salma, Ghina Siti Sunendiari Siti Sunendiari Taufik Muhammad Fakih Tripuspitorini, Fifi Afiyanti Yani Krishnamurti Yayat Karyana