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Jurnal Statistika Universitas Muhammadiyah Semarang
Published by Universitas Muhammadiyah Semarang
ISSN : 23383216     EISSN : 25281070     DOI : -
Core Subject : Science,
Decision Sciences, Operations Research & Management
Focus and Scope a. Statistika Teori, Statistika Komputasi, Statistika terapan b. Matematika Teori dan Aplikasi c. Design of Experiment
Arjuna Subject : Matematika - Pemodelan dan Simulasi
Articles 5 Documents
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Search results for , issue "Vol 9, No 2 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang" : 5 Documents clear
PEMODELAN PERSEPSI PEMBELAJARAN ONLINE MENGGUNAKAN LATENT DIRICHLET ALLOCATION Jerhi Wahyu Fernanda
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 2 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.2.2021.79-85

Abstract

Latent Dirichlet Allocation (LDA) merupakan metode untuk pemodelan topik adalah yang didasarkan kepada konsep probabilitas untuk mencari kemiripan suatu dokumen dan mengelompokkan dokumen-dokumen menjadi beberapa topik atau kelompok.   Metode ini masuk dalam unsupervised learning karena tidak ada label atau target pada data yang dianalisis. Penelitian ini bertujuan untuk mengelompokkan persepsi tentang pembelajaran online ke dalam beberapa topik menggunakan metode LDA. Data penelitian ini adalah data primer yang dikumpulkan melalui formulir online. Hasil analisis menunjukkan bahwa pemodelan LDA menggunakan 6 topik memiliki coherence score paling besar. Hasil visualisasi data text menggunakan wordcloud didapatkan kata tidak memiliki frekuensi kemunculan terbesar. Penentuan jumlah topik yang optimal berdasarkan coherence score, didapatkan pemodelan LDA dengan 6 topik adalah yang paling optimal. secara garis besar terdapat beberapa kata yang saling beririsan dengan topik yang lain. Hasil pemodelan memberikan gambaran bahwa persepsi/pandangan mahasiswa terdapat pembelajaran online terkait pemahaman materi yang diberikan dosen, sinyal atau jaringan internet, kuota, dan tugas. Pada kata-kata terkait pemahaman materi, mahasiswa memberikan pandangan bahwa mereka tidak dapat memahami dengan baik materi yang diberikan oleh dosen.
PERAMALAN DENGAN METODE SARIMA PADA DATA INFLASI DAN IDENTIFIKASI TIPE OUTLIER (Studi Kasus: Data Inflasi Indonesia Tahun 2008-2014) Iin Fadliani; Ika Purnamasari; Wasono Wasono
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 2 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.2.2021.109-116

Abstract

Inflation is defined as rising prices of goods in general and continuously. The effect of inflation on the economy can cause the currency to decline, resulting in the country's economic power becoming weak. Time series data is data arranged in order of time or data collected over time. Changes in the inflation rate tend to make inflation data unstable and affect the forecasting process in the time series data. The method used in this study is the seasonal autoregressive integrated moving (SARIMA) method to predict the time series in one or two periods ahead. This study also used outlier identifiers on models that still have outlier tendencies in residuals. The forecasting results of the SARIMA method become inaccurate when residual data contains outliers. The presence of outlier data in residual data results in residuals is not a normal distribution. The method used obtained the best model results, namely the SARIMA model (0,1,1) (0,1,1)12 with inflation forecast value for January to May 2015 is in the range of 5-6 %. On SARIMA models (0,1,1) (1,1,1)12 and SARIMA models (1,1,0) (2,1,0)12 outliers are detected in residual are Additive Outlier (AO) and Temporary Change (TC) type.
ANALISIS FAKTOR YANG MEMPENGARUHI MAHASISWA DALAM PENGAMBILAN PROGRAM STUDI EKONOMI SYARIAH UIN SULTAN MAULANA HASANUDDIN BANTEN AYU SISKA MARYONI
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 2 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.2.2021.86-95

Abstract

Penelitian ini bertujuan untuk menganalisis faktor-faktor apa saja yang mempengaruhi mahasiswa dalam pengambilan program studi Ekonomi Syariah di Perguruan Tinggi Universitas Islam Negeri Sultan Maulana Hasanuddin Banten. Adapun faktor – faktor yang akan diteliti oleh peneliti antara lain minat, motivasi, status orang tua, pekerjaan yang diharapkan dan pengaruh lingkungan belajar baik di lingkungan rumah, kampus maupun masyarakat. Pada penelitian ini menggunakan metode kuisioner yaitu dengan cara membuat daftar pertanyaan dan memberikannya kepada responden dengan harapan akan memberi respon atas pertanyaan tersebut. Pengukuran variabel  dilakukan dengan skala Likert yang menggunakan metode skorsing. Adapun populasi dalam penelitian ini adalah mahasiswa S1 Fakultas Ekonomi dan Bisnis Islam Jurusan Ekonomi Syariah di Universitas Islam Negeri Sultan Maulana Hasanuddin Banten. Metode pengambilan sampel dilakukan dengan cara accidental sampling yaitu teknik penentuan sampel berdasarkan kebetulan/insidential bertemu dengan peneliti dan dapat digunakan sebagai sampel, bila dipandang orang yang kebetulan ditemui itu cocok sebagai sumber data.
OPTIMASI SETTING PARAMATER CLEANLINESS, KETEBALAN, DAN JENIS CAT PADA MATERIAL BAJA A572 TERHADAP DAYA REKAT CAT Farizi Rachman; Bayu Wiro Karuniawan; Anggie Madhu Firdiandani
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 2 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.2.2021.96-100

Abstract

The manufacturing process consists of several stages, one of them is the finishing stage. The finishing stage at PT. Supra Surya Indonesia consists of surface preparation process and painting process. The adhesion of the paint must meet the criteria to get good protective quality for the product. In the painting process, there are always defective products that must go through the repair stage before packing. Process parameters such as cleanliness level of the material surface and paint thickness need to be considered and the selection of the type of paint will affect the results of painting. Based on the problems above, it is necessary to research the analysis of process parameters that can optimize the value of paint adhesion. This study uses the Taguchi method so that the contribution of cleanliness parameters is 49.6995 %, paint thickness is 5.0014 %, and paint type is 40.4139 %. The optimum combination of parameters is cleanliness Sa 2 1/2, the thickness of 100 µm, and the type of phenolic epoxy paint.
PENGELOMPOKAN DESA ATAU KELURAHAN DI KUTAI KARTANEGARA MENGGUNAKAN ALGORITMA DIVISIVE ANALYSIS Ilham Adnan Kasoqi; Memi Nor Hayati; Rito Goejantoro
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 2 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.2.2021.101-108

Abstract

Potential Villages (PODES) provide data on the existence, availability and development of the potential of each government administrative area. In order to make it easier for governments to make policies for a region, it is necessary to group the village and sub-districts. Cluster analysis is an analysis that aims to group objects based on the information that found in the data. One of the cluster analysis methods is the divisive analysis, which is a hierarchical grouping method with a top-down approach, where all objects are placed in one cluster and then sequentially divided into separate groups. This research aim to group villages or sub-districts in Kutai Kartanegara based on the determinants of village backwardness and obtaining the silhouette coefficient value from the optimal cluster analysis using the divisive analysis algorithm. The data used is the 2018 PODES data in Kutai Kartanegara and used 15 variables from natural and environmental factors, facilities infrastructure and access factors as well as socio-economic factors of the population. The results of the optimal cluster formed in the grouping of villages or sub-districts in Kutai Kartanegara using the divisive analysis method are 2 clusters. Cluster 1 consisting of 230 villages or sub-districts and cluster 2 consisting of 2 sub-districts. Silhouette coefficient value for data validation from clustering village or sub-districts in Kutai Kartanegara using the divisive analysis method produces 2 clusters is 0,744 which states that the cluster structure formed in this grouping is a strong structure.

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