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All Journal Sodality: Jurnal Sosiologi Pedesaan Media Kedokteran Hewan Jurnal Kesehatan Ibu dan Anak Majalah Ilmiah Matematika dan Statistika (MIMS) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Veteriner dan Biomedis Scientific Journal of Informatics Indonesian Journal of Statistics and Its Applications International Journal of Oil Palm
Sachnaz Desta Oktarina
Departemen Statistika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, IPB, Bogor, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Analisis Komparatif Sosial, Ekonomi, dan Ekologi antara Komunitas Desa Sawit dan Desa Non Sawit Tonny, Fredian; Desta Oktarina, Sachnaz; Sipayung, Tungkot; Ulfa Aulia, Risnayanti; Maziah, Lily
Sodality: Jurnal Sosiologi Pedesaan Vol. 10 No. 3 (2022): Sodality: Jurnal Sosiologi Pedesaan
Publisher : Departement of Communication and Community Development Sciences, Faculty of Human Ecology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22500/10202241776

Abstract

This study aimed to analyze the level of social, economic, and ecological progress of the Oil Palm Village communities and compare the level of social, economic, and ecological progress between the Oil Palm Village and Non-Oil Palm Village communities. Indonesia is one of the major palm oil-producing countries in the world. Palm oil has brought economic benefits nationally and also to local communities. However, in its development, there has been a controversy surrounding the palm oil commodity, namely in the case of Indonesian palm oil which is related to the issues of deforestation and territorialization due to the economic interests of palm oil versus the existence of forest areas. This study used a Quantitative Approach with Secondary Data Methods from primary sources (Ministry of Village, Development of Disadvantage Region, and Transmigration, BPS, and Directorate General of Plantation) with the village communities as the unit of analysis. As many as 524 village communities were selected from the population of Oil Palm Villages and Non-Oil Palm Villages in eight provinces of Indonesia’s oil palm centers with a combination of Purposive Multistage Sampling and Propensity Score Matching methods. Descriptive analysis, comparative analysis, analysis of the difference in progress using the Difference in Difference (DID) model, and the binary logistic regression method were carried out in this study. The results of the study revealed the facts that there has been an increase in social, economic, and ecological progress in various Oil Palm Village communities. The level of social, economic, and ecological progress of Oil Palm Village communities is higher than that of Non-Oil Palm Village communities. These facts indicate that the community sustainability level of the Oil Palm Village communities is superior to that of the Non-Oil Palm Village communities.
How does COVID-19 Impact Oil Palm Management Practices in Indonesia? Ratnawati Nurkhoiry; Sachnaz Desta Oktarina
International Journal of Oil Palm Vol. 3 No. 2 (2020): May 2020
Publisher : Indonesian Oil Palm Society /IOPS (Masyarakat Perkelapa-sawitan Indonesia /MAKSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35876/ijop.v3i2.49

Abstract

Disrupted economy due to COVID-19 pandemic has been spilled to multifaceted sectors. Agriculture, more specifically oil palm sector was also hit by the impact of the catastrophe. This study is aimed to decipher the effect of COVID-19 pandemic to the management of oil palm plantation. The pandemic has caused the mobility and morbidity of people in such a way that exacerbated distribution of input factor, harvesting process, and transporting activities. Through online survey to 59 farmers consist of smallholder, government, and private estates, the study indicated that there was salient change of limiting activities particularly during immature and mature stages. Hence, the production of fresh fruit bunch (FFB) also decreased by 15% compared to business as usual as measured on monthly bases. Although the magnitude of production change was not statistically significance, planters still suffered from declining FFB farm gate price. On average, they received 5% lower selling price of FFB as a consequence of contracted CPO demand from prominent importing countries such as China, India, and Italy. The lower selling farm gate price has caused the income shocks to the farmers. Thus, quite large number of them experienced either turn-over or cost efficiency at the expense of fertilizer input. It is perpetuating the vicious cycle of lower smallholder attainable FFB yield. For a group that is at the high risk of infections as well, this circumstances has bring about concerns to Indonesian palm oil development, especially in terms of replanting realization and biodiesel blending progress in the long run
Winsorization for Outliers in Clustering Non-Cyclical Stocks with K-Means and K-Medoids: Winsorization untuk Penanganan Pencilan dalam Penggerombolan Saham Sektor Consumer Non-Cyclical dengan K-Means dan K-Medoids Ardhani, Naura Tirza; Notodiputro, Khairil Anwar; Oktarina, Sachnaz Desta
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i1p46-60

Abstract

Non-cyclical consumer sector stocks are often chosen by investors because the products in this sector are essential products that always in demand by society. Therefore, the demand for these products tends to be stable and defensive or less affected by economic shocks. However, it does not guarantee that every stock in this sector has good performance, thus it is necessary to group stocks based on their fundamental indicators in the form of financial ratios. This research aims to identify the best method by considering outliers and determining the clusters with the best fundamental performance as a recommendation for investors to make the right investment decisions. The data used in this study is secondary data with observations in the form of 50 non-cyclical consumer sector stocks. The variables used are Earning per Share, Return on Equity, Return on Assets, Debt to Equity Ratio, Price to Earnings Ratio, and Price to Book Value. The clustering results indicated that K-Medoids is the best clustering method, both on the data before and after handling extreme outliers with winsorization approach. However, the optimum number of clusters before and after winsorization are different, with 3 and 6 clusters. Considering the influence of extreme outliers and to get a more informative clustering result, the clustering result after the application of winsorization technique was chosen, which resulted in 6 clusters. Cluster 1, which consists of AALI, GGRM, INDF, and SGRO can be recommended because it has excellent fundamental performance, especially in terms of Earning per Share in 2022.
Low Birth Weight Classification With Synthetic Minority Over Sampling Technique Random Forest Oktarina, Sachnaz Desta; Wijayanto, Hari; Yarah, Helena Ramadhini
Jurnal Kesehatan Ibu dan Anak Vol. 17 No. 1
Publisher : Poltekkes Kemenkes Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29238/kia.v17i1.1802

Abstract

Low birth weight (LBW) is defined as a condition where the birth weight is less than 2500 grams. Infants born with LBW conditions are more susceptible to disease and have a higher risk of dying at an early age. LBW conditions that are prone to unbalanced data can be classified using the Synthetic Minority Oversampling Technique (SMOTE) random forest method. The analysis was processed on the 2017 Indonesian Demographic and Health Survey (IDHS) data to identify important variables in predicting the incidence of LBW. The results showed that the SMOTE random forest model provided an accuracy value of 79.84%, sensitivity of 30.99%, specificity of 83.6%, and AUC of 62%. Important variables in predicting the incidence of LBW were the number of antenatal care visits, wealth quantile, maternal age at delivery, iron supplementation, marital status, and twins’ birth.
Seroprevalence, Accuracy and Precision Value of Brucellosis Surveillence at The Region Area of Balai Karantina Pertanian Kelas I Balikpapan Faizal Rafiq; Notodipuro, Khairil Anwar; Oktarina, Sachnaz Desta; Mualifah, Laily Nissa; Pandansari, Niken; Hapsari, Linda Dwi
Media Kedokteran Hewan Vol. 34 No. 3 (2023): Media Kedokteran Hewan
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/mkh.v34i3.2023.157-170

Abstract

The Technical Implementation Unit alai Karantina Pertanian Kelas I Balikpapan plays a role in efforts to prevent the entry, spread, and release of HPHK. Direct observation was performed by surveying the target brucellosis in the region of Balai Karantina Pertanian Kelas I Balikpapan. This surveillance aimed to determine the seroprevalence of brucellosis and to support the maintenance of brucellosis status in East Kalimantan. Sampling in areas with reported clinical symptoms and brucellosis reactors. The sampling areas were based on the region of Balai Karantina Pertanian Kelas I Balikpapan in Balikpapan City, North Penajam Paser Regency, Paser Regency, and Kutai Kartanegara Regency. The method for calculating the number of samples to detect the disease uses the Rose Bengal Test (RBT) and Complement Fixation Test (CFT). The test results showed a seroprevalence of 0.29%, positive and negative predictive values of 50% and 99,7%, respectively, an accuracy value of 99.11%, and a precision of 50%. The test performance based on the accuracy value was excellent because it had a value of 99.11%, which means that the ability of the CFT to detect all samples tested correctly was 99.11%. The test carried out using the CFT test on this surveillance had a precision or test consistency of 50%, and the sensitivity and specificity were 50% and 99.7%, respectively.
Perbandingan penerapan optimasi SGDM dan Adam pada model CNN dengan arsitektur VGG19 dan ResNet-50 dalam memprediksi penyakit paru-paru pneumonia Oktarina, Sachnaz Desta; Alpharofi, Deswita Nur; Hasanah, Delita Nur; Kurniawan, Rizky; Muzakki, Faiz Aji; Najdmuddin, Muhammad Tsaqif; Anisa, Rahma
Majalah Ilmiah Matematika dan Statistika Vol. 25 No. 2 (2025): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v25i2.53746

Abstract

Pneumonia is a leading cause of death among children under five, accounting for 14% of fatalities. Chest X-ray analysis is a key method for diagnosis, but many developing countries have only one radiologist per million people, making timely detection difficult. To address this challenge, Convolutional Neural Networks (CNN) offer a viable solution due to their ability to analyze visual data efficiently. This study evaluates two CNN architectures, VGG19 and ResNet-50, considering their effectiveness in pneumonia detection. Both models were trained using two different optimizers, SGDM and Adam, to determine the best combination for accurate classification. Results using test data indicate that VGG19 with the Adam optimizer achieves the highest accuracy at 90%, surpassing other models which recorded 62%, 77%, and 84% without overfitting. This highlights the potential of artificial intelligence driven diagnostic tools in bridging healthcare gaps and improving pneumonia detection in resource-limited settings. Keywords: Classification, CNN, Optimizer, PneumoniaMSC2020: 62
Pengaruh Pemberian Salep Chlorella vulgaris Terhadap Penyembuhan Luka Sayatan pada Mencit (Mus musculus albinus) Wahyuni, Sri; Notodiputro, Khairil Anwar; Oktarina, Sachnaz Desta; Mualifah, Laily Nissa Atul
Jurnal Veteriner dan Biomedis Vol. 2 No. 1 (2024): Maret
Publisher : Sekolah Kedokteran Hewan dan Biomedis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jvetbiomed.2.1.16-21.

Abstract

Penelitian ini bertujuan untuk mengetahui pengaruh salep Chlorella vulgaris terhadap proses penyembuhan luka sayatan mencit (Mus musculus albinus) berdasarkan waktu yang dibutuhkan untuk menyembuhkan luka dan perubahan morfologi luka dibandingkan kontrol. Penelitian ini menggunakan metode Rancangan Acak Lengkap (RAL) dengan menggunakan 25 ekor mencit sebagai hewan uji yang dibagi menjadi 5 kelompok yaitu; 3 kelompok perlakuan (C. vulgaris salep 5%, C. vulgaris salep 10%, C. vulgaris salep 15%) dan 2 kelompok kontrol (plasebo dan proses penyembuhan normal). Mencit dilukai dengan scalpel-blade sepanjang 1 cm sampai fascia. Luka diolesi salep C. vulgaris dua kali sehari dan diamati setiap hari dari hari ke 1 sampai hari ke 14. Semua data kuantitatif diuji secara statistik menggunakan ANOVA dan data kualitatif disajikan secara deskriptif. Hasil penelitian menunjukkan bahwa terdapat perbedaan yang signifikan pada 5 kelompok (P>0,05). Terdapat perbedaan antara kelompok perlakuan (C. vulgaris salep 5%, C. vulgaris salep 10%, C. vulgaris salep 15%) dan kelompok kontrol. Hasilnya salep C. vulgaris berpengaruh terhadap proses penyembuhan luka sayatan mencit (M. m. albinus) dibandingkan kelompok kontrol dengan kandungan ekstrak C. vulgaris 10% paling baik untuk menyembuhkan luka dengan cepat.
Performance of SARIMA, LSTM, GRU and Ensemble Methods for Forecasting Nickel Prices Irdayanti; Notodiputro, Khairil Anwar; Oktarina, Sachnaz Desta
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.32225

Abstract

Purpose: There are several forecasting methods, including SARIMA, LSTM, and GRU, which are often claimed to exhibit strong performance in capturing patterns in time series data. However, few studies have conducted direct comparisons among these methods. Therefore, it is necessary to conduct a performance evaluation using empirical data, particularly nickel prices data. This study also aims to improve forecasting performance by combining prediction outputs from deep learning-based models. Methods: This study utilized data on monthly global nickel prices from January 1990 to May 2025. The models developed include SARIMA, LSTM, GRU, and two ensemble approaches: Weighted Averaging and Bayesian Model Averaging (BMA). Model validation was conducted using walk-forward validation with a sliding window approach to evaluate each model’s generalization performance on out-of-sample validation data. The performance was evaluated using MAPE, RMSE, and MAE. Result: The BMA Ensemble approach shows the best performance in forecasting nickel prices, with a MAPE value of 5.39%, RMSE of 1897.84, and MAE of 1133.96. Prediction validation produces MAPE values below 10%, which indicates that the forecasting results are accurate. The ensemble BMA approach is able to produce more accurate and stable predictions compared to other models. Novelty: This study offers a novel approach combining LSTM and GRU through ensemble methods to forecast global nickel prices using monthly historical data from 1990 to 2025. In contrast to previous studies that relied on single models, the proposed method with the ensemble BMA approach demonstrates improved forecasting accuracy and stability.
A Study on Prediction Intervals Produced Using Quantile Regression Forest With and Without Variable Selection Megawati, Megawati; Sartono, Bagus; Oktarina, Sachnaz Desta
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.34392

Abstract

Quantile Regression Forest (QRF) is a method that utilizes the random forest algorithm to estimate the conditional distribution of response variables and form quantile prediction intervals. However, when there is a high correlation between covariates, QRF performance may decrease due to the multicollinearity effect, thereby reducing the accuracy of the prediction interval for the target variable. In linear models, multicollinearity must be addressed because it can cause large variances. This study contributes to enhancing the reliability of prediction intervals in correlated data through the integration of adaptive-LASSO with QRF. Specifically, it examines the role of variable selection by the adaptive LASSO method on the performance of the QRF prediction interval in the simulated data, and the best model obtained in the study is then applied to predict the interval in the productivity data of oil palm fresh fruit bunches. The results of the study show that variable selection is proven to produce coverage close to the target prediction interval. In addition, the QRF model with variable selection applied to the productivity data of oil palm fresh fruit bunches produces a good prediction interval.
Co-Authors Alpharofi, Deswita Nur Anisa, Rahma Ardhani, Naura Tirza Bagus Sartono Faizal Rafiq Fredian Tonny Nasdian Hapsari, Linda Dwi Hari Wijayanto Hasanah, Delita Nur Irdayanti Maziah, Lily Megawati - Mualifah, Laily Nissa Mualifah, Laily Nissa Atul Muzakki, Faiz Aji Najdmuddin, Muhammad Tsaqif Notodipuro, Khairil Anwar Pandansari, Niken Ratnawati Nurkhoiry Rizky Kurniawan Sipayung, Tungkot Sri Wahyuni Ulfa Aulia, Risnayanti Yarah, Helena Ramadhini