Variance最新文献

{"title":"CLASSIFICATION OF THE GEOCHEMICAL COMPOSITION OF METEORITE OF PUNGGUR (ASTOMULYO) BY k-NEAREST NEIGHBOR ALGORITHM","authors":"Triyana Muliawati","doi":"10.30598/variancevol5iss2page185-192","DOIUrl":"https://doi.org/10.30598/variancevol5iss2page185-192","url":null,"abstract":"The fall of a meteorite in Astomulyo Village, Punggur, Lampung Province in early 2021 is an interesting topic for further study. This rare object has been suggested to have a unique geochemical composition and a special connection with other meteorites. We aimed to trace its classification by comparing it with other well-known meteorites studied previously. We approach the classification process using the k-nearest neighbor algorithm. The database used 211 represents the geochemical data for each known meteorite group from chemical analyses of meteorites. As a result, we identified that with a k-value = 5 and the proportion of test data 5/95 (in %), the geochemical composition of this meteorite is relatively close to that of the H-type chondrite group with a value accuracy of 91.67%. These results are consistent with the fact that the meteorite of Punggur has a high total iron and metallic composition.","PeriodicalId":485700,"journal":{"name":"Variance","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135931037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MODEL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) UNTUK MERAMALKAN INFLASI INDONESIA","authors":"Yonlib Weldri Arnold Nanlohy, Samsul Bahri Loklomin","doi":"10.30598/variancevol5iss2page201-208","DOIUrl":"https://doi.org/10.30598/variancevol5iss2page201-208","url":null,"abstract":"Inflasi merupakan kondisi perekonomian suatu negara dimana terjadi peningkatan harga barang dan jasa secara terus menerus dalam jangka waktu tertentu. Akibat dari inflasi pengeluaran masyarakat dalam memenuhi kebutuhan pokok semakin meningkat. Permasalahan inflasi harus dapat dikendalikan untuk menjaga stabilitas perekonomian negara. Oleh karena itu perlu adanya perkiraan atau peramalan mengenai tingkat Inflasi Indonesia. Salah satu metode yang dapat digunakan untuk peramalan inflasi Indonesia yaitu metode Autoregressive Integrated Moving Average (ARIMA). Metode ARIMA merupakan suatu metode peramalan berdasarkan pola data secara historis. Hasil analisis diperoleh model ARIMA (2,1,2) untuk peramalan inflasi di Indonesia dengan nilai RMSE hasil peramalan data out-sample untuk jangka waktu 7 periode ke depan adalah 1,826864 sehingga model ARIMA (2,1,2) dapat digunakan sebagai model peramalan inflasi di Indonesia..","PeriodicalId":485700,"journal":{"name":"Variance","volume":"119 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135931038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MODEL GABUNGAN (ANSAMBEL) SARIMA DAN JARINGAN SARAF TIRUAN UNTUK PERAMALAN BEBAN LISTRIK","authors":"Mega Silfiani","doi":"10.30598/variancevol5iss2page193-200","DOIUrl":"https://doi.org/10.30598/variancevol5iss2page193-200","url":null,"abstract":"This study aims to investigate the efficacy of employing artificial neural networks in conjunction with a seasonal autoregressive integrated moving average (SARIMA) ensemble for forecasting electrical load. The SARIMA ensemble comprises members generated by varying autoregressive orders or moving averages. Subsequently, these SARIMA ensemble members are integrated using artificial neural networks. The datasets encompass monthly electrical load data pertaining to households, businesses, industries, and the public, spanning from January 2016 to December 2020. The findings demonstrate that across various categories, SARIMA ensemble-based artificial neural networks demonstrated superior predictive performance compared to alternative models. Future research endeavors should focus on exploring diverse methodologies for both creating and amalgamating ensemble members.","PeriodicalId":485700,"journal":{"name":"Variance","volume":"7 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135931044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MODEL REGRESI DATA PANEL PADA TINGKAT KRIMINALITAS DI PROVINSI NUSA TENGGARA BARAT DENGAN MENGGUNAKAN FIXED EFFECT MODEL","authors":"Nurwahyuni Indah Nurwahyuni, Zulhan Widya Baskara, Nur Asmita Purnamasari","doi":"10.30598/variancevol5iss2page169-184","DOIUrl":"https://doi.org/10.30598/variancevol5iss2page169-184","url":null,"abstract":"
 
 
 
 Kriminalitas merupakan salah satu permasalahan yang banyak terjadi dilingkungan masyarakat. Pada tahun 2020, Nusa Tenggara Barat menempati posisi kedelapan dengan jumlah kejahatan terbanyak di Indonesia. Agar angka kriminalitas tidak mengalami kenaikan maka perlu diketahui faktor-faktor yang mempengaruhinya. Dalam penelitian ini, faktor yang digunakan yaitu tingkat pengangguran, pendidikan dan jumlah penduduk di sepuluh kabupaten/kota di Nusa Tenggara Barat pada tahun 2016-2020. Penelitian ini bertujuan untuk mengetahui model regresi data panel dan bagaimana pengaruh faktor tingkat pengangguran, tingkat pendidikan dan jumlah penduduk terhadap angka kriminalitas. Metode analisis yang digunakan adalah Fixed Effect Model dengan pendekatan Least Square Dummy Variable (LSDV). Faktor pengangguran dan tingkat pendidikan memiliki pengaruh yang tidak signifikan terhadap angka kriminalitas. Sedangkan faktor jumlah penduduk berpengaruh signifikan terhadap angka kriminalitas.
 Kata Kunci: Fixed Effect Model, jumlah penduduk, kriminalitas, Least Square Dummy Variable (LSDV), pengangguran, tingkat pendidikan.
 
 
 
 
 Abstract: Crime is one of the most common problems in society. In 2020, West Nusa Tenggara occupies the eighth position with the highest number of crimes in Indonesia. So that the crime rate does not increase, it is necessary to know the factors that influence it. In this study, the factors used were unemployment rate, education and population in ten districts cities in West Nusa Tenggara in 2016-2020. This study aims to determine the panel data regression model and how factors influence the unemployment rate, education level and population on crime rates. The analytical method used is the Fixed Effect Model with the Least Square Dummy Variable (LSDV) approach. Unemployment and education level factors have no significant effect on the crime rate. While the population factor has a significant effect on the crime rate.
 Keywords: Fixed Effect Model, Least Square Dummy Variable (LSDV), crime, unemployment, education level, population.
 
 
 
","PeriodicalId":485700,"journal":{"name":"Variance","volume":"7 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135931039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WORKFORCE GROUPING IN COMPLETING PROJECTS WITH INTERN WORK ACTIVITY LOG DATA","authors":"Brandon Anggawidjaja, Faizah Sari, Ahmad Fuad Zainuddin","doi":"10.30598/variancevol5iss2page131-138","DOIUrl":"https://doi.org/10.30598/variancevol5iss2page131-138","url":null,"abstract":"This study consists of an attempt to optimize the K-Means Clustering Algorithm and calculating the Full Time Equivalent (FTE) of each cluster based on intern's daily work log data. The optimization will be done by using some of K-Means Clustering’s validation method to estimate the best K clusters of the data. The validation methods that will be used to optimize the algorithm are Elbow Criterion Method and Silhouette Score Index. The initial k cluster will be formed and evaluated using Davies Bouldin Index analysis. The divided clusters are supposed to be classified by the rate of complexity of each project. The calculated FTE will be used to estimate the workload for the current workforce. This estimation is hoped to help companies decide in their hiring decision.","PeriodicalId":485700,"journal":{"name":"Variance","volume":"7 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135931041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ANALISIS PENYEBARAN JUMLAH KASUS PMK PADA HEWAN TERNAK SAPI DI KABUPATEN LOMBOK TENGAH MENGGUNAKAN INDEKS MORAN TAHUN 2022","authors":"Ayu Septiani, Ristu Haiban Hirzi, Nawwarun Uyun Fikriah","doi":"10.30598/variancevol5iss2page159-168","DOIUrl":"https://doi.org/10.30598/variancevol5iss2page159-168","url":null,"abstract":"Salah satu sektor penting dalam pertumbuhan perekonomian Indonesia adalah sektor peternakan. Keberhasilan peternakan baik besar maupun kecil, dipengaruhi oleh kesehatan ternaknya. Sejak tahun 1990 Indonesia dinobatkan sebagai negara bebas PMK oleh OIE sejak tahun 1990. Namun, pada awal bulan april 2022 PMK mulai mewabah kembali secara luas dan menjangkit hewan ternak khususnya ternak sapi. Dampak dari wabah ini, banyak peternak yang mengalami kerugian yang signifikan. Wabah PMK ini menjadikan Provinsi NTB berada di rangking ke 6 dengan total kasus aktif PMK. Sebaran kasus PMK di NTB, Lombok Tengah merupakan kasus terbanyak dengan jumlah 28.612 ekor dari populasi sapi sebanyak 323.232 ekor. Metode yang peneliti gunakan ini adalah analisis jumlah penyebaran PMK dengan pendekatan peta sebaran. Adapun tujuan dilakukan penelitian ini adalah untuk membantu pemerintah terkait mengetahui penyebaran PMK. Berdasarkan analisis data dapat diketahui pola penyebaran penyakit mulut dan kuku di Kabupaten Lombok Tengah jika dilihat dari moran scatterplot menunjukkan bahwa pola sebaran PMK berada pada kuadran I dan III.","PeriodicalId":485700,"journal":{"name":"Variance","volume":"130 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135930690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PENENTUAN PREMI MURNI DI KABUPATEN KEPAHIANG PROVINSI BENGKULU DENGAN MEMPERHITUNGKAN PELUANG KEJADIAN GEMPA BUMI DAN RASIO KERUSAKAN BANGUNAN","authors":"Tiara Yulita, Chaterine Theresia Lubis, Agus Sofian Eka Hidayat","doi":"10.30598/variancevol5iss2page147-158","DOIUrl":"https://doi.org/10.30598/variancevol5iss2page147-158","url":null,"abstract":"Indonesia is a country that is very vulnerable to earthquakes, one of which is in Bengkulu Province, especially Kepahiang Regency. To deal with risks or losses caused by earthquakes, insurance can be purchased. Therefore, in this study, earthquake insurance premiums will be determined by taking into account the probability of an earthquake occurring and the ratio of damage to buildings in Kepahiang Regency. The PSHA (Probabilistic Seismic Hazard Analysis) method is used to determine the probability of an earthquake occurring. In the PSHA process, earthquake data will be collected and analyzed to identify earthquake sources, characterize earthquake sources, and calculate earthquake hazard (the probability of an earthquake). Damage data on buildings will be processed to obtain a ratio of building damage. After that, a pure premium will be obtained, by multiplying the EADR (Expected Annual Damage Ratio) value with the sum insured of the building, where EADR is the estimated level of annual damage due to earthquakes in an area","PeriodicalId":485700,"journal":{"name":"Variance","volume":"116 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135930691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IDENTIFIKASI JENIS KENDARAAN BERMOTOR DENGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORKS","authors":"Muhammad Athoillah, Rani Kurnia Putri","doi":"10.30598/variancevol5iss2page109-116","DOIUrl":"https://doi.org/10.30598/variancevol5iss2page109-116","url":null,"abstract":"Deteksi jenis kendaraan bermotor memainkan peran sentral dalam pengaturan lalu lintas, penegakan hukum, keamanan, dan sistem transportasi pintar. Dengan kemampuan luar biasa dalam mendeteksi dan mengklasifikasikan kendaraan dengan akurat, pihak berwenang dapat mengoptimalkan waktu sinyal lalu lintas, pengelolaan jalur, dan aliran lalu lintas secara efisien. Deteksi jenis kendaraan juga memberikan dukungan penting dalam penegakan peraturan lalu lintas dan memverifikasi kepatuhan kendaraan terhadap batasan tertentu, termasuk jalur kendaraan bersama, tol, dan peraturan parkir. Di sisi keamanan, teknologi ini berperan krusial dalam mengidentifikasi kendaraan mencurigakan, mencegah ancaman, dan meningkatkan keselamatan di area sensitif. Salah satu pendekatan populer dalam mendukung sistem deteksi jenis kendaraan bermotor otomatis adalah menggunakan algoritma deep learning, khususnya Convolutional Neural Network (CNN). Dengan kemampuannya mengenali pola dan fitur pada citra kendaraan menggunakan struktur jaringan syaraf tiruan, CNN mampu memberikan hasil yang luar biasa. Penelitian ini bertujuan mengembangkan sistem otomatis deteksi jenis kendaraan bermotor dengan menggunakan algoritma CNN. Hasil penelitian menunjukkan kinerja yang sangat baik, dengan rata-rata presisi sebesar 97,00%, sensitivitas/recall sebesar 97,60%, spesifisitas sebesar 97,59%, dan akurasi sebesar 97,30%.","PeriodicalId":485700,"journal":{"name":"Variance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135930694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PENERAPAN ALGORITMA ITERATIVE DICHOTOMISER 3 (ID3) DALAM KLASIFIKASI FAKTOR RISIKO PENYAKIT DIABETES MELITUS","authors":"Ferdina Ferdina, Neva Satyahadewi, Dadan Kusnandar","doi":"10.30598/variancevol5iss2page139-146","DOIUrl":"https://doi.org/10.30598/variancevol5iss2page139-146","url":null,"abstract":"Algoritma Iterative Dichotomiser 3 (ID3) adalah sebuah metode yang digunakan untuk membuat pohon keputusan. Pohon keputusan merupakan salah satu metode klasifikasi dengan model prediksi menggunakan struktur pohon. International Diabetes Federation pada tahun 2021 menyatakan bahwa Indonesia menduduki posisi kelima dalam kasus diabetes terbanyak, dengan jumlah penyandang diabetes sebanyak 19,47 juta penduduk. Tujuan penelitian ini adalah menerapkan Algoritma ID3 dan menentukan akurasinya dalam klasifikasi faktor risiko diabetes melitus. Data yang digunakan dalam penelitian ini adalah data hasil tes kesehatan karyawan di Kota Banyuwangi, Jember, Kediri, Madiun, Malang, Sidoarjo dan Surabaya yang kemudian dibagi menjadi data training dan data testing. Atribut klasifikasi yang digunakan dalam penelitian ini adalah jenis kelamin, usia, gula darah sewaktu (GDS), High Density Lipoprotein (HDL), Low Density Lipoprotein (LDL), dan trigliserida. Berdasarkan hasil pengujian klasifikasi Algoritma ID3 menggunakan data training dan software R Studio, diperoleh variabel dengan nilai information gain tertinggi adalah Gula Darah Sewaktu (GDS). Berdasarkan hasil perhitungan, nilai akurasi yang diperoleh dari metode Algoritma ID3 adalah sebesar 90%. Akurasi yang diperoleh, dapat disimpulkan bahwa keakuratan Algoritma ID3 tergolong baik dalam klasifikasi faktor risiko penyakit diabetes melitus.","PeriodicalId":485700,"journal":{"name":"Variance","volume":"130 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135930697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RETURN PORTOFOLIO OPTIMAL DENGAN PENDEKATAN SINGLE INDEX MODEL, TREYNOR BLACK MODEL, DAN BLACK-LITTERMAN MODEL","authors":"Adri Arisena","doi":"10.30598/variancevol5iss2page117-130","DOIUrl":"https://doi.org/10.30598/variancevol5iss2page117-130","url":null,"abstract":"Membentuk portofolio optimal adalah metode yang dapat membantu para investor meminimalkan risiko dan mengoptimalkan keuntungan. Beberapa model untuk portofolio optimal termasuk Single Index Model (SIM), Treynor Black Model (TBM), dan Black-Litterman Model (BLM). SIM didasarkan pada pengamatan bahwa harga sekuritas berfluktuasi sejalan dengan indeks pasar. Pada TBM, seorang investor dapat melihat bahwa model ini kurang fokus pada nilai beta tetapi lebih berfokus pada risiko tidak sistematis. BLM menggabungkan elemen data historis dan pandangan investor untuk membentuk prediksi baru tentang portofolio sebagai dasar pemodelan. Prediksi pandangan dalam penelitian ini menggunakan pendekatan time series ARIMA dan GARCH. Tujuan dari penelitian ini adalah untuk membentuk tingkat pengembalian portofolio optimal dengan menggunakan SIM, TBM, dan BLM berdasarkan pandangan tunggal investor serta kombinasi pandangan beberapa investor dengan pendekatan ARIMA dan GARCH.","PeriodicalId":485700,"journal":{"name":"Variance","volume":"368 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135765831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}