cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Nusar Hajarisman
Contact Email
statistika@unisba.ac.id
Phone
+628124569091
Journal Mail Official
statistika@unisba.ac.id
Editorial Address
Program Studi Statistika Universitas Islam Bandung Jl. Ranggagading No. 8 Bandung 40116 Phone: 081572198581 Email: nusarhajarisman@unisba.ac.id
Location
Kota bandung,
Jawa barat
INDONESIA
STATISTIKA
Published by Universitas Islam Bandung
ISSN : 14115891     EISSN : 25992538     DOI : https://doi.org/10.29313/statistika.v23i1.1628
Core Subject : Science, Education,
Decision Sciences, Operations Research & Management Mathematics
STATISTIKA published by Department of Statistics, Faculty of Mathematics and Natural Sciences, Bandung Islamic University as pouring media and discussion of scientific papers in the field of statistical science and its applications, both in the form of research results, discussion of theory, methodology, computing, and review books. Published biannually in May and November each.
Arjuna Subject : -
Articles 91 Documents
 4   5   6   7   8 
Analisis Ketertinggalan Desa di Provinsi Papua dan Papua Barat Menggunakan Association Rule Mining Primanda, Etsa; Oktora, Siskarossa Ika
Statistika Vol. 24 No. 1 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i1.2302

Abstract

ABSTRAK Pada hakikatnya, pembangunan dimaksudkan untuk mengupayakan kondisi kehidupan yang lebih layak. Namun, data Indeks Pembangunan Desa 2018 menunjukkan persentase desa tertinggal di Pulau Papua paling banyak dibandingkan pulau lainnya. Terlebih lagi, penelitian mengenai karakteristik utama ketertinggalan desa di wilayah tersebut belum dilakukan secara komprehensif. Oleh sebab itu, penelitian ini bertujuan untuk mengetahui gambaran umum desa tertinggal, serta menganalisis karakteristik utama ketertinggalan desa di Provinsi Papua dan Papua Barat. Sumber data yang digunakan adalah data indikator Indeks Pembangunan Desa 2018 yang diperoleh dari Subdirektorat Statistik Ketahanan Wilayah Badan Pusat Statistik (BPS) berdasarkan Pendataan Potensi Desa 2018. Metode analisis yang digunakan adalah analisis deskriptif menggunakan diagram batang dan peta tematik, serta teknik data mining menggunakan association rule mining. Hasil analisis menunjukkan Kabupaten Tolikara, Provinsi Papua dan Kabupaten Pegunungan Arfak, Provinsi Papua Barat memiliki persentase desa tertinggal yang tertinggi. Sebagian besar desa tertinggal di Provinsi Papua dan Papua Barat berada di wilayah dengan topografi dataran tinggi dan pegunungan. Hasil association rule mining menunjukkan karakteristik utama ketertinggalan desa sebagian besar kabupaten di Provinsi Papua adalah pelayanan kesehatan, sarana transportasi, dan infrastruktur ekonomi. Sementara itu, karakteristik utama ketertinggalan desa sebagian besar kabupaten di Provinsi Papua Barat adalah pelayanan kesehatan. ABSTRACT The goal of development is to seek more decent living conditions. However, the Village Development Index 2018 data shows that the percentage of rural underdevelopment on Papua Island is the highest compared to other islands. Moreover, researchers have yet to conduct comprehensive research in the region on the main characteristics of rural underdevelopment. Therefore, this study aims to observe the general description of rural underdevelopment and analyze the main characteristics of rural underdevelopment in Papua and West Papua Provinces. The data source used is the Village Development Index 2018 indicator data from the Regional Resilience Statistics Sub-Directorate of Badan Pusat Statistik (BPS) based on the Village Potential Data 2018. The analytical methods used are descriptive analysis using bar charts, thematic maps, and data mining techniques, namely association rule mining. The results show that the percentages of rural underdevelopment in Tolikara Regency, Papua Province, and Arfak Mountains Regency, West Papua Province, are higher among other regions. Areas characterized by highlands and mountainous terrain in Papua and West Papua Provinces concentrate most of the rural underdevelopment. Then, the results of association rule mining show that the main characteristics of rural underdevelopment in most districts in Papua Province are health services, transportation facilities, and economic infrastructure. Meanwhile, the main characteristic of rural underdevelopment in most districts in West Papua Province is health services.
Studi Komparasi Regresi Logistik Biner dan K-Nearest Neighbor Pada Kasus Prediksi Curah Hujan Rahmawati, Fitri; Amanah, Fitri; Fallo, Sefri Imanuel
Statistika Vol. 24 No. 1 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i1.2739

Abstract

ABSTRAK Perubahan iklim yang sedang terjadi di berbagai belahan dunia sebagai akibat dari pemanasan global telah menyebabkan ketidakpastian cuaca. Salah satu perubahan yang dirasakan adalah intensitas curah hujan. Hal ini mengakibatkan prediksi akan curah hujan menjadi penting untuk dilakukan. Ada beberapa teknik analisis data yang digunakan untuk prediksi curah hujan, diantaranya klasifikasi. Pada penelitian ini, dengan menggunakan variabel temperatur, kelembapan, lamanya penyinaran, dan kecepatan angin, akan dilakukan prediksi terhadap klasifikasi curah hujan di Kota Bogor. Model yang digunakan adalah Regresi Logistik Biner dan K-Nearest Neighbor. K yang digunakan pada model K-Nearest Neighbor yaitu 1 hingga 18. Untuk membandingkan kedua model, dibentuk confusion matrix yang selanjutnya digunakan untuk menghitung akurasi model. Akurasi model Regresi Logistik Biner sebesar 92,746%, adapun akurasi model K-Nearest Neighbor adalah sebesar 94,81865%. Dengan demikian, pada penelitian ini model K-Nearest Neighbor lebih baik dibandingkan model Regresi Logistik Biner. ABSTRACT Climate change due to global warming occurring in all parts of the world makes the weather unpredictable. One of the changes felt is the intensity of rainfall. This makes it important to predict rainfall. There are several data analysis techniques used to predict rainfall, including classification. In this research, using the variables temperature, humidity, duration of sunlight, and wind speed, predictions will be made on the classification of rainfall in the city of Bogor. The models used are Binary Logistic Regression and K-Nearest Neighbor. The K used in the K-Nearest Neighbor model is 1 to 18. To compare the two models, a confusion matrix is formed and then used to calculate the model accuracy. The accuracy of the Binary Logistic Regression model is 92.746%, while the accuracy of the K-Nearest Neighbor model is 94,81865%. Thus, in this research the K-Nearest Neighbor model is better than the Binary Logistic Regression model.
Analisis Variabel-Variabel yang Menjelaskan Tingkat Prokrastinasi Akademik pada Mahasiswa FMIPA Universitas XYZ Widyaningsih, Yekti; Rahmawati, Aprilia; Soemartojo, Saskya Mary
Statistika Vol. 24 No. 1 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i1.3054

Abstract

ABSTRAK Mahasiswa diharapkan untuk menempuh pendidikan sarjananya dengan baik dan selesai dalam tepat waktu. Sebagai mahasiswa mempunyai aktivitas cukup banyak di luar rutinitas kuliah sudah menjadi hal yang lazim. Dengan banyaknya rutinitas, mahasiswa seringkali menunda belajar atau menyelesaikan tugas yang diberikan oleh dosennya inilah yang disebut dengan prokrastinasi akademik. Prokrastinasi akademik pada mahasiswa dapat berdampak pada penurunan prestasi akademiknya. Tujuan penelitian ini adalah mengetahui variabel-variabel yang menjelaskan tingkat prokrastinasi akademik pada mahasiswa, mengetahui profil mahasiswa yang mempunyai tingkat prokrastinasi akademik yang tinggi, dan mengetahui perbedaan antara kedua metode yang digunakan berdasarkan urutan variabel-variabel yang signifikan menjelaskan tingkat prokrastinasi akademik pada mahasiswa. Variabel yang diduga menjelaskan tingkat prokrastinasi akademik adalah jenis kelamin, tempat tinggal, kondisi fisik, kondisi psikologis, kondisi lingkungan, motivasi belajar, persepsi mahasiswa, dukungan sosial orang tua, dan dukungan sosial teman sebaya. Penelitian ini menggunakan metode Analisis Regresi Linier Berganda dan Classification and Regression Tree (CRT). Penelitian ini memanfaatkan data primer yaitu 660 mahasiswa FMIPA Universitas XYZ yang dipilih melalui metode purposive sampling. Hasil penelitian menyimpulkan bahwa variabel-variabel yang secara signifikan menjelaskan tingkat prokrastinasi akademik mahasiswa FMIPA Universitas XYZ adalah jenis kelamin, kondisi fisik, kondisi psikologis, motivasi belajar, persepsi mahasiswa, dukungan sosial orang tua, dan dukungan sosial teman sebaya. Profil mahasiswa yang memiliki tingkat prokrastinasi akademik yang tinggi yaitu mahasiswa dengan kondisi fisik dan kondisi psikologis yang buruk, serta dukungan sosial orang tua yang rendah. Dan juga adanya perbedaan urutan variabel-variabel yang signifikan antara metode Regresi Linier Berganda dan CRT, tetapi variabel kondisi fisik berada pada urutan pertama kedua metode tersebut. ABSTRACT Students are expected to be able to undertake their undergraduate studies satisfactorily and graduate as scheduled.  As a student, it is normal having with numerous activities outside academic routine. Consequently, students often delay studying and completing the tasks given by their lecturers. This is called academic procrastination. Academic procrastination may lead to a declining academic achievement. This study aimed to determine variables that affect academic procrastination levels, to find out the profile of students with high levels of academic procrastination, and to the difference between the two methods on the sequence of significant variables explains the level of academic procrastination of students. The variables considered to affect the level of academic procrastination include gender, living place, physical conditions, psychological conditions, environmental conditions, learning motivation, student perception, parental social support, and peer social support. The methods used are Multiple Linear Regression and Classification and Regression Tree (CRT). This study used primary data, namely 660 FMIPA students of University of XYZ obtained through purposive sampling.  The results showed that the variables that significantly affect the level of academic procrastination of FMIPA students of University of XYZ include gender, physical conditions, psychological conditions, learning motivation, student perception, parental support, and peer support. Students who demonstrate a high level of academic procrastination are characterized by poor physical and psychological conditions, as well as low parental support. In addition, there is a significant difference in the sequence of variables between the Multiple Linear Regression method and CRT, but both have one thing in common, that is, the highest variable is physical condition.
Monte Carlo Based Sampling Distribution of Annual Rate of Exceedance for Earthquake Insurance Darwis, Sutawanir; Hajarisman, Nusar; Suliadi; Widodo, Achmad; Diahsty Marasabessy, Munira; Arya Ramadhan, Iqbal
Statistika Vol. 24 No. 1 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i1.3173

Abstract

ABSTRACT Seismic hazard expressed in terms of annual rate of exceedance and is used to calculate the earthquake insurance premium. Annual rate of exceedance is a complicated function of magnitudes, distances from site to earthquake sources and attenuation. Due to its complexity, determination of exact sampling distribution of earthquake insurance premium is not an easy task. This research proposes Monte Carlo simulation approach to determine the sampling distribution of earthquake insurance premium. Annual rate of exceedance was simulated first and then the insurance premium calculated based on simulation of annual rate of exceedance. The simulation involves quantifying synthetic catalogue similar to historical catalogue. Its simulation is conducted in order to construct annual rate of exceedance as an indicator of earthquake risk used in earthquake insurance. The simulation of 25 iteration and sample size of 100 shows that the sampling distribution of insurance premium is skewed to the right. The idea of using Monte Carlo simulation to study the sampling distribution of annual rate of exceedance is the originality of the study and the study contributes to the methodology of earthquake insurance.
Analisis Faktor-Faktor yang Berhubungan dengan Kejadian Stres Pada Mahasiswa Tingkat Akhir S1 Matematika di Universitas Negeri Medan Sitepu, Eliya; Juliana Tampubolon; Sudianto Manulang; Sisti Nadia Amalia
Statistika Vol. 24 No. 1 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i1.3257

Abstract

ABSTRAK Stres adalah respons adaptif individual terhadap stimulus yang dianggap mengancam, dapat dialami oleh siapa saja, termasuk mahasiswa tingkat akhir. Penelitian ini fokus pada faktor-faktor yang memengaruhi tingkat stres mahasiswa tingkat akhir S1 Matematika di Universitas Negeri Medan, dengan penekanan pada penyusunan tugas akhir (skripsi). Beberapa faktor, seperti tuntutan akademik, masalah pribadi (manajemen waktu, motivasi, dukungan keluarga), dan aturan batas masa studi, diidentifikasi sebagai penyebab stres.Metode penelitian menggunakan pendekatan kuantitatif non-eksperimental dengan uji korelasi Rank Spearman. Hasil penelitian menunjukkan korelasi cukup kuat (0.380) antara faktor kejadian stres dan tingkat stres mahasiswa. Meskipun tidak mencapai tingkat signifikansi yang umum, temuan ini mengindikasikan bahwa mahasiswa yang mengalami berbagai faktor kejadian stres cenderung memiliki tingkat stres yang lebih tinggi. Penelitian ini memberikan wawasan mendalam tentang kompleksitas faktor-faktor yang berkontribusi terhadap stres mahasiswa tingkat akhir, menunjukkan perlunya pendekatan holistik dan intervensi terintegrasi dari perguruan tinggi untuk membantu mahasiswa mengelola stres dengan lebih efektif. Temuan ini juga memberikan dasar bagi pengembangan program dukungan dan pembinaan untuk membantu mahasiswa mengatasi tantangan akademik dan pribadi. ABSTRACT Stress is an individual adaptive response to a stimulus that is perceived as threatening, can be experienced by anyone, including final year students. This research focuses on the factors that influence the stress level of final year undergraduate mathematics students at Medan State University, with an emphasis on the preparation of the final project (thesis). Several factors, such as academic demands, personal problems (time management, motivation, family support), and study period limit rules, were identified as causes of stress.The research method used a non-experimental quantitative approach with the Spearman Rank correlation test. The results showed a moderately strong correlation (0.380) between stress event factors and students' stress levels. Although not reaching the usual level of significance, this finding indicates that college students who experience multiple stressful event factors tend to have higher stress levels. This study provides an in-depth insight into the complexity of factors contributing to final year university students' stress, suggesting the need for a holistic approach and integrated interventions from universities to help students manage stress more effectively. The findings also provide a basis for the development of support and coaching programs to help college students overcome academic and personal challenges.
Prediksi Ketersediaan Tenaga Listrik di Jawa Tengah dengan Forecast Linear dan Error Trend Seasonality menggunakan Excel Ulfah Mediaty Arief; Sri Sukamta; Dewi Anggriani; Moh. Umar Dani Atik
Statistika Vol. 24 No. 1 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i1.3277

Abstract

ABSTRAK Dalam era pertumbuhan ekonomi dan kemajuan teknologi yang pesat, kebutuhan akan pasokan listrik yang konsisten semakin mendesak. Penelitian ini membandingkan metode prediksi error trend seasonality (ETS) dan prediksi linear untuk mengantisipasi ketersediaan listrik di Jawa Tengah, menggunakan data dari 2011 hingga 2021. Evaluasi menunjukkan bahwa metode prediksi linear lebih akurat dengan persentase kesalahan lebih rendah dibandingkan error trend seasonality (ETS). Prediksi menunjukkan tren positif peningkatan ketersediaan listrik, membuka peluang pengembangan industri dan pertumbuhan ekonomi regional. Keandalan prediksi linear memberikan dasar yang solid untuk pengambilan keputusan di sektor energi. Rekomendasi untuk meningkatkan keandalan prediksi di masa depan mencakup pemantauan dan pembaruan data, serta melibatkan pemangku kepentingan dan ahli energi dalam pengambilan keputusan. Penelitian ini diharapkan memberikan panduan bagi pembuat kebijakan dan pihak terkait dalam mengelola ketersediaan energi listrik secara efisien di Jawa Tengah. ABSTRACT In an era of rapid economic growth and technological progress, the need for a consistent electricity supply is increasingly urgent. This research compares the error trend seasonality prediction (ETS) method and linear prediction to anticipate electricity availability in Central Java, using data from 2011 to 2021. The evaluation shows that the linear prediction method is more accurate with a lower error percentage than error trend seasonality (ETS). Predictions show a positive trend of increasing electricity availability, opening up opportunities for industrial development and regional economic growth. The reliability of linear predictions provides a solid basis for decision making in the energy sector. Recommendations to improve the reliability of future predictions include monitoring and updating data, as well as involving stakeholders and energy experts in decision making. This research is expected to provide guidance for policy holders and related parties in managing the availability of electrical energy efficiently in Central Java.
Analisis Faktor-Faktor yang Memengaruhi Status Diabetes Mellitus pada Pra Lansia dan Lansia di Indonesia Menggunakan Model Regresi Logistik Biner Eva Fridiyani Putri; Kismiantini
Statistika Vol. 24 No. 1 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i1.3319

Abstract

ABSTRAK Indonesia merupakan negara yang menempati urutan ke-5 dengan penderita diabetes mellitus sekitar 19,5 juta pada tahun 2021 berdasarkan laporan International Diabetes Federation 2021. Menurut publikasi BPS tahun 2022, sejak tahun 2021 Indonesia telah memasuki struktur penduduk tua, sekitar 1 dari 10 penduduk adalah lanjut usia (lansia). Penduduk Indonesia yang berusia antara 45-59 tahun termasuk kategori pra lansia dan di atas 60 tahun termasuk kategori lansia. Pada umumnya, penyakit yang dialami lansia merupakan penyakit tidak menular, seperti diabetes mellitus, jantung, dan hipertensi. Tujuan dari penelitian ini untuk mengetahui faktor risiko yang memengaruhi status diabetes mellitus (ya/tidak) pada pra lansia dan lansia menggunakan model regresi logistik biner. Analisis data menggunakan model regresi logistik biner karena variabel responsnya berupa biner. Hasil penelitian menunjukkan bahwa faktor risiko yang berpengaruh signifikan terhadap logit peluang menderita diabetes mellitus adalah variabel IMT (normal, gemuk, obesitas), tingkat pendidikan terakhir (tinggi), status sosial ekonomi subjektif (menengah), usia lansia, aktivitas fisik, hipertensi, mengonsumsi makanan manis, pemeriksaan kesehatan umum dan daerah tempat tinggal. Responden yang termasuk pada kategori lansia memiliki peluang lebih tinggi 1,383 kali untuk menderita diabetes mellitus dibandingkan dengan yang pra lansia. ABSTRACT Indonesia is a country that ranks 5th with around 19.5 million people with diabetes mellitus in 2021 based on the International Diabetes Federation 2021 report. According to the BPS publication in 2022, since 2021 Indonesia has entered an old population structure, about 1 in 10 residents are elderly (elderly). The Indonesian population aged between 45-59 years is categorized as pre-elderly and above 60 years is categorized as elderly. In general, diseases experienced by the elderly are non-communicable diseases, such as diabetes mellitus, heart disease, and hypertension. The purpose of this study is to determine the risk factors that affect diabetes mellitus status (yes / no) in pre elderly and elderly using a binary logistic regression model. The data was analysed using binary logistic regression model because the response variable is binary. The results showed that the risk factors that had a significant effect on the logit of the chances of suffering from diabetes mellitus were the IMT variable (normal, obese, obese), the last level of education (high), subjective socioeconomic status (medium), elderly age, physical activity, hypertension, consuming sweet foods, general health checks and the area of residence. Respondents who are in the elderly category have a higher chance of 1.383 times to suffer from diabetes mellitus compared to those who are pre-elderly.
ARIMA (p, d, q) Modeling for Predicting Exports of Fresh and Chilled Fish Based on Market Conditions and Main Destination Countries : The Case of Indonesia 2012-2022 Handayani, Vitri Aprilla; Sulistyono, Eko; Sunarsono, Hery; Arafi, Adamsyam; Harahap, Diana Sari
Statistika Vol. 24 No. 1 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i1.3376

Abstract

ABSTRACT Fresh and chilled fish are the largest contributor to Indonesia's fishery product exports, accounting for a share of 45% or around USD 2.2 billion in 2021. The main destination countries for Indonesia's fresh and chilled fish exports include China, the United States, Japan, and other European countries. This research aims to analyze the factors influencing the export value of Indonesia's fresh and chilled fish, as well as to identify and evaluate the ARIMA (p, d, q) model based on historical data from 2012-2022. The result is an ARIMA (4,2,2) model with a MAPE value of 2.208 and a predicted value for the 2023 period of 4351 tons. This is in line with the large exports of fresh fish from Indonesia to various destination countries.
Perbandingan Penggunaan Model Regresi Linear dan Nonlinear dalam Mendeterminasi Daya Simpan Panas (DSP) Gerabah Pengembangan Salnuddin; Susanto, Adi Noman; Bemba, Jefry
Statistika Vol. 24 No. 1 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i1.3466

Abstract

ABSTRAK Daya simpan panas (DSP) gerabah vorno merupakan suatu kebutuhan untuk meningkatkan produksi sagu lempeng. Metode eksprimen penambahan aluvial pada pembuatan gerabah dengan komposisi tanah liat 40 %, aluvial 25% dan pasir pantai 35%  sebagai gerabah pengembangan diharapkan dapat meningkatkan DSP gerabah. Keterbatasan instrumen pengujian DSP secara teknik, membutuhkan solusi secara statistik dalam mendeterminasi DSP. Perbandingan hasil analisis dari pendekatan metode linear dan non linear pada data perubahan suhu lempengan membentuk karakteristik tertentu, olehnya itu dibutuhkan identifikasi analisis statistik yang tepat dalam mendeterminasi DSP dengan akurasi tinggi. Hasil penelitian menunjukkan bahwa penggunaan material aluvial pada pembuatan gerabah meningkatkan berat jenis lempengan gerabah. Koefisien determinasi (R2) dari keseluruhan persamaan metode linear dan non linear memberikan linearity yang tinggi (R2 > 75%), akurasi tertinggi dengan nilai mean absolute percent error (MAPE) < 10% dijumpai pada tren persamaan kubik dan logaritmik. Pendekatan tren kubik menunjukkan DSP gerabah pengembangan lebih lambat 7% dibandingkan dengan gerabah rujukan, sedangkan penggunaan tren logaritik lebih lambat 33%. Perlu dilakukan analisis data dengan perhitungan transfer panas untuk membuktikan DSP dengan pendekatan statistik. ABSTRACT The heat storage capacity (HSC) of vorno pottery is a necessity to increase the production of sago plates. The experimental method of adding alluvial to pottery making with a composition of 40% clay, 25% alluvial and 35% beach sand as development pottery is expected to increase the DSP of pottery. The limitations of technical DSP testing instruments require a statistical solution in determining DSP. Comparison of analysis results from linear and non-linear method approaches on slab temperature change data forms a specific characteristic, therefore it is necessary to identify the right statistical analysis in determining DSP with high accuracy. . The results show that the use of alluvial materials in pottery making increases the specific gravity of pottery slabs. The coefficient of determination (R2) of all equations of linear and non-linear methods gave high linearity (R2 > 75%), the highest accuracy with mean absolute percent error (MAPE) value < 10% was found in the cubic and logarithmic trend equations. The cubic trend approach showed that the heat retention of the development pottery was 7% slower than the reference pottery, while the use of the logarithmic trend was 33% slower. It is necessary to analyse the data with heat transfer calculations to prove the HSC with a statistical approach.
Forecasting Spare Part pada Commercial Vehicle PT XYZ dengan Klasifikasi ADI-CV Irfan Rizki, Muhammad; Yenny Maya Dora; Wahidiyat Suyudi; Yanthy Mardiana
Statistika Vol. 24 No. 1 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i1.3546

Abstract

ABSTRAK After Sales memiliki kontribusi positif pada pertumbuhan profit permintaan spare part suatu perusahaan. PT. XYZ merupakan agen tunggal pemegang merek mobil kendaraan niaga di Indonesia dengan visi menjadi market share terbesar secara global dipasar internasional. Peranan penting yang dilakukan PT. XYZ dalam upaya memenuhi seluruh kebutuhan permintaan domestik atau ekspor dengan langkah menerapkan rencana inventaris yang mencakup peramalan kebutuhan suku cadang setiap bulan. Pada kondisi saat ini metode yang digunakan oleh perusahaan hanya menggunakan metode peramalan dengan rata-rata 6 bulan terakhir atau Moving Average yang digunakan untuk menyiapkan seluruh suku cadang PT. XYZ. Metode ini dinilai kurang efektif karena ketika terjadinya bentuk permintaan spare part yang memiliki tingkat variasi tinggi maka penyimpangan pada hasil peramalan menyebabkan back order terhadap pelanggan dan loss sales yang dapat mempengaruhi nilai suatu perusahaan yaitu fast and easy process. Berdasarkan permasalahan tersebut, maka PT. XYZ membutuhkan perbaikan terhadap mekanisme peramalan spare part-nya. Tujuan dari dilakukan penelitian yaitu menghasilkan metode klasifikasi spare part berdasarkan bentuk permintaannya dan memutuskan metode peramalan yang paling tepat untuk masing-masing kelompok spare part dengan langkah membandingkan enam metode peramalan yaitu metode Croston Optimized, TSB, SESOpt, ADIDA, IMAPA, Moving Average. Semua metode peramalan akan dibandingkan atas dasar parameter nilai forecasting error. Pada hasil yang didapat menunjukkan metode Croston Optimized memiliki hasil yang lebih baik dari metode moving average dengan memperbaiki kesalahan sebesar 7%. ABSTRACT After Sales has a positive contribution to the profit growth of a company's spare part demand. PT XYZ is the sole agent of commercial vehicle car brand holders in Indonesia with a vision to become the largest market share globally in the international market. An important role played by PT XYZ in an effort to meet all domestic or export demand needs by implementing an inventory plan that includes forecasting the need for spare parts on a monthly basis. In the current condition, the method used by the company only uses a forecasting method with the average of the last 6 months or Moving Average which is used to prepare all spare parts of PT. XYZ. This method is considered less effective because when there is a form of spare part demand that has a high level of variation, deviations in forecasting results cause back orders to customers and loss of sales which can affect the value of a company, namely fast and easy process. Based on these problems, PT XYZ needs improvements to its spare part forecasting mechanism. The purpose of the research is to produce a spare part classification method based on the form of demand and decide the most appropriate forecasting method for each spare part group by comparing six forecasting methods, namely the Croston Optimized method, TSB, SESOpt, ADIDA, IMAPA, Moving Average. All forecasting methods will be compared on the basis of the forecasting error value parameter. The results obtained show that the Croston Optimized method has better results than the moving average method by correcting an error of 7%.

Page 6 of 10 | Total Record : 91

 4   5   6   7   8 

Filter by Year

2021 2025


Reset
Filter By Issues
All Issue Vol. 25 No. 2 (2025): Statistika Vol. 25 No. 1 (2025): Statistika Vol. 24 No. 2 (2024): Statistika Vol. 24 No. 1 (2024): Statistika Vol. 23 No. 2 (2023): Statistika Vol. 23 No. 1 (2023): Statistika Vol. 22 No. 2 (2022): Statistika Vol. 22 No. 1 (2022): Statistika Vol. 21 No. 2 (2021): Statistika