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All Journal Jurnal Matematika BIMASTER Epsilon: Jurnal Matematika Murni dan Terapan CESS (Journal of Computer Engineering, System and Science) BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) KABILAH : Journal of Social Community Jurnal Pendidikan Sejarah dan Riset Sosial Humaniora (KAGANGA) BULETIN AL-RIBAATH Jambura Journal of Mathematics GERVASI: Jurnal Pengabdian kepada Masyarakat Sarwahita : Jurnal Pengabdian Kepada Masyarakat M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Variance : Journal of Statistics and Its Applications International Journal of Economics, Business and Accounting Research (IJEBAR) Jurnal Pendidikan Dasar Perkhasa Jurnal Pengabdian kepada Masyarakat Nusantara Journal of Mathematics Education and Science AR-RIAYAH : Jurnal Pendidikan Dasar Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi CENDEKIA : Jurnal Ilmu Pengetahuan Journal of Community Development Empiricism Journal Jurnal Eurekamatika Jurnal Forum Analisis Statistik Pattimura International Journal of Mathematics (PIJMath) STATISTIKA Bina Bahari : Jurnal Pengabdian Kepada Masyarakat Equiva
Neva Satyahadewi
Program Studi Statistika Fakultas MIPA Universitas Tanjungpura, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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Journal : Variance : Journal of Statistics and Its Applications

 1 
PENERAPAN ALGORITMA ITERATIVE DICHOTOMISER 3 (ID3) DALAM KLASIFIKASI FAKTOR RISIKO PENYAKIT DIABETES MELITUS Ferdina Ferdina; Neva Satyahadewi; Dadan Kusnandar
VARIANCE: Journal of Statistics and Its Applications Vol 5 No 2 (2023): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol5iss2page139-146

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.
IMPLEMENTASI WEB SCRAPING UNTUK ULASAN PADA TWITTER MENGGUNAKAN ASOSIASI TEKS (STUDI KASUS: FILM KKN DI DESA PENARI) Frans Xavier Natalius Antoni; Neva Satyahadewi; Hendra Perdana
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 1 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss1page21-28

Abstract

Pengguna twitter biasanya memberikan komentar yang berisi mengenai ulasan suatu film yang sedang tayang. Teknik yang dapat digunakan untuk mengambil komentar pada twitter yaitu Web Scraping. Penelitian ini bertujuan untuk mengimplementasikan Web Scraping dalam mengumpulkan data pada twitter dan mengimplementasikan Asosiasi Teks untuk mendapatkan informasi antar kata yang terbentuk. Penelitian ini menggunakan komentar yang berisi mengenai ulasan Film KKN di Desa Penari pada tanggal 30 April 2022. Komentar yang diperoleh tidak semua berisi ulasan, sehingga perlu dilakukan seleksi terhadap komentar tersebut. Hasil seleksi dari 866 komentar diperoleh sebanyak 116 ulasan positif dan 83 ulasan negatif. Data yang diperoleh dari komentar tidak bisa langsung dianalisis, sehingga perlu melalui tahap text preprocessing. Adapun tahap text preprocessing yaitu cleansing data, case folding, spelling normalization, filtering, dan tokenizing. Setelah melalui tahap text preprocessing, ulasan tersebut kemudian dianalisis untuk mendapatkan informasi yang penting dengan menggunakan Asosiasi Teks. Hasil Asosiasi Teks untuk ulasan positif diperoleh informasi bahwa penonton memberikan penilaian terhadap tokoh, akting dan sinematografi yang bagus, kemudian film yang ditayangkan juga sesuai dengan cerita thread pada twitter, dan sinematografi juga keren. Sedangkan untuk ulasan negatif penonton memberikan penilaian bahwa Film KKN di Desa Penari, film yang biasa dan hantu yang ditayangkan juga kurang seram.
A ORDINAL LOGISTIC REGRESSION BAGGING FOR MODELING AND CLASSIFICATION OF THE NUTRITIONAL STATUS OF TODDLERS IN SOUTHEAST PONTIANAK SUB-DISTRICT Sista, Sekar Aulia; Kusnandar, Dadan Tonny; Satyahadewi, Neva
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss2page195-204

Abstract

Although Pontianak's 2022 stunting rate of 19.7% is higher than the RPJMN's 2020–2024 target of 14%, this is still significant. The categories of stunts are very short (severely stunted), short (stunted), normal, and high, based on a high index of the body by age (TB/U). Ordinal Logistic Regression is one classification that can be used to group stunts based on the TB/U index. This approach makes the unstable parameter. Use the bagging to get stable parameters. The study aims to model and classify toddlers' nutritional status using the TB/U index. Utilizing secondary data for 150 toddlers from Pontianak Tenggara's UPT Puskesmas Parit Haji Husin II. This will monitor kids' growth from 24 to 59 months in 2022. Response factors include short, very short, normal, and high. The mother's job position, birth weight, length, and gender are the predictive variables. Due to imbalanced data utilized in the first analysis using Ordinal Logistics Regression, a decent model, and the final classification result, they used the Bagging OLR ensemble method. The study's findings are a very effective model using OLR Bagging, with an accuracy rate of 99.33%, a sensitivity value of 98.91%, and a specificity value of 98.52%. The results also revealed significant variables that influence the mother's employment status and the birth length variable.
IMPLEMENTATION GRID SEARCH OF RBF AND POLYNOMIAL ON SUPPORT VECTOR REGRESSON FOR CLOSING STOCK PRICES PREDICTION ON PT INDOFARMA (INAF) Salsabilla, Arla; Satyahadewi, Neva; Andani, Wirda
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss2page133-142

Abstract

Stocks represent evidence of ownership of an asset. The highly volatile nature of stock prices makes it difficult for investors to predict stock prices, necessitating the analysis of stock investments. This research aims to forecast for the next 30 days the closing price of PT Indofarma (INAF) stocks using the best model, and the accuracy level of the employed model was analyzed based on the data from the last seven years. The research used the Support Vector Regression (SVR) method, which is known for its capability to handle nonlinear data through kernel functions. The Radial Basis Function (RBF) and polynomial kernels are used in this case. The challenge with SVR lies in determining the optimal hyperparameter, which can be addressed through hyperparameter tuning using grid search. The research results show that the best model is the SVR kernel RBF model with optimal hyperparameter C=1,γ=0.01, and ε=0.01. Based on the performance evaluation results of the best model, the MAPE, MSE, and MAE values are equal to 1.537%,1483.936, and 23.409.
APPLICATION OF THE QUEST AND CHAID METHODS IN CLASSIFYING STUDENT GRADUATION Banu, Syarifah Syahr; Sulistianingsih, Evy; Debataraja, Naomi Nessyana; Satyahadewi, Neva
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss2page155-164

Abstract

Graduation is the final result of the learning process during the course. Student graduation time is affected by many factors. Whether or not the time of student graduation is appropriate is an important thing that must be considered. Graduating well and on time is one measure of success in the learning process. This research aims to build a student graduation classification model by applying the QUEST (Quick, Unbiased, and Efficient, Statistical Tree) and CHAID (Chi-squared Automatic Interaction Detection) methods, examining the factors that affect student graduation, and comparing the classification results of the two methods. Both methods produce output in the form of tree diagrams, making it easier to interpret. Based on the classification tree formed from the two methods, four final nodes of the classification tree were generated, and three categories were grouped. Factors that affect student graduation include age and IPK. The classification results show that the percentage of classification accuracy for student graduation with QUEST and CHAID methods is 76.1%.
FORECASTING THE COMBINED STOCK PRICE INDEX (IHSG) USING THE RADIAL BASIS FUNCTION NEURAL NETWORK METHOD Fitriawan, Della; Satyahadewi, Neva; Andani, Wirda
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page83-92

Abstract

The capital market is one of the most critical factors in national economic development in Indonesia, as many industries and companies have previously used the capital market as a medium to absorb investment so that their financial position can be strengthened. The main indicator that can reflect the performance of the capital market is the Composite Stock Price Index (IHSG). The IHSG can be used to assess the general situation occurring in the market. Data IHSG is data obtained from the past and used to predict the future, also called time series data. Predictions on IHSG data need to be made so that investors can easily see capital market movements and know the policies that will be taken in the future. The Radial Basis Function Neural Network (RBFNN) method is used. RBFNN aims to get more efficient results because this method does not need to make the data stationary. The analysis results were carried out on a secondary data sample size of 1114 data, which obtained the highest forecasting price of Rp6157,619 on August 2, 2023. Meanwhile, the lowest forecast price on August 5, 2023, is IDR 5564,828 from August 1, 2023, to August 5, 2023.
Co-Authors . Apriansyah Aditya Handayani Afghani Jayuska Afghany Jayuska Alqaida Yusril Alvin Octavianus Halim Amriani Amir Amriani Amir Amriani Amir Amriani Amir Andani, Wirda Anisa Putri Ayuni Apriliyanti, Rita Aprizkiyandari, Siti Ardhitha, Tiffany Ari Hepi Yanti Arsyi, Fritzgerald Muhammad Ashari, Asri Mulya Asri Mulya Ashari Asty Fistia Ningrum Atikasari, Awang Aulia Puteri Amari Bambang Kurniadi Banu, Syarifah Syahr ciptadi, wahyudin Dadan Kusnandar Dadan Kusnandar Dadan Kusnandar David Jordy Dhandio Debataraja, Naomi Nessyana Della Zaria Desriani Lestari Desriani Lestari Desriani Lestari Dhandio, David Jordy Dinda Lestari Dwi Nining Indrasari Dwinanda, Maria Welita Esta Br Tarigan Evy Sulistianingsih Ewaldus Okta Ezra Amarya Aipassa Ferdina Ferdina Feriliani Maria Nani Fitriawan, Della Frans Xavier Natalius Antoni Fransisca Febrianti Sundari Fransiska Fransiska Giovani Parasta Riswanda Grikus Romi Gusti Eva Tavita Gusti Eva Tavita Hairil Al-Ham Hamzah, Erwin Rizal Hanin, Noerul Harimurti, Puspito Harnanta, Nabila Izza Hastri Sastia Wuri Helena, Shifa Hendra Perdana Hendrianto, El Herina Marlisa Huda, Nur'ainul Miftahul Huriyah, Syifa Khansa Ibnur Rusi Ikha Safitri Imro'ah, Nurfitri Imro’ah, Nurfitri Imtiyaz, Widad Indry Handayany Isra’ Sagita Jawani Jawani Karlina, Sela Kusnandar, Dadan Tonny Lucky Hartanti Lucky Hartanti Lucky Hartanti M. Deny Hafizzul Muttaqin Maga, Fahmi Giovani Margareta, Tiara Margaretha, Ledy Claudia Marlisa, Herina Marola, Geby Martha, Shantika Mega Sari Juane Sofiana Mega Sari Juane Sofiana Mega Tri Junika Millennia Taraly Misrawi Misrawi Muhammad Ahyar Muhammad Radhi Muliadi Muliadi Muslimah (F54210032) Nabil, Ilhan Nail Nanda Shalsadilla Naomi Nessyana Debataraja Naomi Nessyana Debataraja Noerul Hanin Nona Lusia Nugrahaeni, Indah Nur Asih Kurniawati Nur Asiska Nur'ainul Miftahul Huda Nurfitri Imro'ah Nurfitri Imro’ah Nurhalita Nurhalita Nurmaulia Ningsih NUR’AINUL MIFTAHUL HUDA Oktaviani, Indah Ovi Indah Afriani Paisal Paisal Pertiwi, Retno Pratama, Aditya Nugraha Preatin Preatin Putri Putri Putri, Aulia Nabila Qalbi Aliklas R Puspito Harimurti Radhi, Muhammad Rafdinal Rafdinal Rahadi Ramlan Rahmadanti, Putri Rahmanita Febrianti Rusmaningtyas Rahmawati, Fenti Nurdiana Rahmi Fadhillah Ramadhan, Nanda Ramadhania, Wahida Reni Unaeni Retnani, Hani Dwi Ria Andini Ria Fuji Astuti Rina Rina Risky Oprasianti Rita Kurnia Apindiati Rivaldo, Rendi Riza Linda Rizki Nur Rahmalita Rosi Kismonika Roslina Rosi Tamara Rovi Christova Safira, Shafa Alya Salsabilla, Arla Santika Santika Sary, Rifkah Alfiyyah Seftiani Seftiani Selvy Putri Agustianto Setyo Wir Rizki Setyo Wira Rizki Setyo Wira Rizki Setyo Wira Rizki Shantika Martha Shantika Martha Sinaga, Steven Jansen Sintia Margun Sista, Sekar Aulia Siti Aprizkiyandari Siti Aprizkiyandari, Nurul Qomariyah, Shantika Martha, Siti Hardianti Steven Jansen Sinaga Suci Angriani Sukal Minsas Sukal Minsas Syuradi syuradi Tamtama, Ray Taraly, Inggriani Millennia Tiara, Dinda Wahyu Diyan Ramadana Wahyudin Ciptadi Warsidah Warsidah Warsidah, Warsidah Wilda Ariani Wirda Andani Yopi Saputra Yudhi Yuliono, Agus Yumna Siska Fitriyani Yundari, Yundari Yuveinsiana Crismayella Zakiah, Ainun