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Akbar Rizki
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Xplore: Journal of Statistics
Published by Institut Pertanian Bogor
ISSN : 23025751     EISSN : 26552744     DOI : https://doi.org/10.29244/xplore
Core Subject : Science, Education, Engineering,
Decision Sciences, Operations Research & Management Engineering Mathematics
Xplore: Journal of Statistics diterbitkan berkala 3 (tiga) kali dalam setahun yang memuat tulisan ilmiah yang berhubungan dengan bidang statistika. Artikel yang dimuat berupa hasil penelitian atau kajian pustaka dalam bidang statistika dan atau penerapannya. ISSN: 2302-5751 Mulai Desember 2018, Xplore: Journal of Statistics mendapatkan ISSN baru untuk media online (eISSN:2655-2744) sesuai dengan SK no. 0005.26552744/JI.3.1/SK.ISSN/2018.12 - 13 Desember 2018. Maka sesuai ketentuan pada SK tersebut, edisi Xplore: Journal of Statistics mulai Desember 2018 akan dimulai menjadi Volume 7 dan No 3. eISSN: 2655-2744
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 6 Documents
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Search results for , issue "Vol. 11 No. 1 (2022)" : 6 Documents clear
Regresi Elastic Net dengan Peringkasan Luas untuk Mengukur Keakuratan Alat Non-Invasive Produk Tahun 2017 dan 2019 Fariz Mufti Rusdana; Itasia Dina Sulvianti; . Erfiani
Xplore: Journal of Statistics Vol. 11 No. 1 (2022)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (869.716 KB) | DOI: 10.29244/xplore.v11i1.848

Abstract

Diabetes melitus is one of dangerous disease because it’s hard to be cured. This is shows it’s important for everyone to always control and checking their blood glucose levels to prevent make the diabetes melitus is getting worse. Non-invasive biomarking team from IPB currently developing blood glucose device measurement with non-invasive method. Now, the non-invasive biomarking team from IPB already created 2 products, design product for 2017’s and 2019’s with the output in the form of a residual intensity spectrum with respect to the time-domain. Therefore, calibration modeling is needed to predict blood glucose level. The best calibration modeling method for 2017’s device discovered by Herianti (2020) with elastic net regression and DDC algorithm for resolve the outlier. In 2019, measuring the blood glucose level were using different tools. This research aims to determine a more stable tool for measuring the blood glucose level with non-invasive method from 2 available tools, and to determine a more accurate summarization method of the intensity residual spectrum. More stable tool for measuring the blood glucose level is a 2017’s device. The summarization method in this research uses a trapezoidal area and 3 digit summarization approach. The result showed that the 2 summarization method didn’t have a significant different in accuracy.
Analisis Unggahan Media Sosial pada Instagram Rachel Vennya Menggunakan Metode Importance Performance Else Virdiani; Aam Alamudi; Yenni Angraini
Xplore: Journal of Statistics Vol. 11 No. 1 (2022)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1101.038 KB) | DOI: 10.29244/xplore.v11i1.850

Abstract

Instagram is one of the social media applications that can publish photos or videos for its users. Rachel Vennya is a well-known Instagram user who has more than five million followers. This research was conducted to see the expected posts by Rachel Vennya's followers on Instagram. Through the importance-performance analysis (IPA) it will be known the types of posts that are interesting and need to be increased in publication. This study's two IPA approaches, namely expected performance analysis (EPA) and importance-performance matrix analysis (IPMA). The results of each analysis are then mapped into a Cartesian diagram so that it is known that several posts increase follower loyalty and posts that need to be increased or decreased. After comparing the two Cartesian diagrams, it is known that there is no difference in the placement of variables between the two analyzes. Posts that deserve to be maintained include Motivation, Cooking, Family, and posts considered excessive in the publication are Business and Endorsements. Furthermore, customer satisfaction index (CSI) analysis was carried out to see follower satisfaction. The CSI value obtained is 72.69, which indicates the follower satisfaction index belongs to the satisfied criteria.
Penerapan Support Vector Machine dengan SMOTE Untuk Klasifikasi Sentimen Pemberitaan Omnibus Law Pada Situs CNNIndonesia.com Widiananda Putri Hutami; Hari Wijayanto; Itasia Dina Sulvianti
Xplore: Journal of Statistics Vol. 11 No. 1 (2022)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (382.721 KB) | DOI: 10.29244/xplore.v11i1.852

Abstract

The declaration of the omnibus law reaped the pros and cons in the community. In a situation like this, the media should be neutral. One of the media that still maintains neutrality is Detik (Rumata 2017). Detik owns several channels such as detikNews, detikFinance, and CNN Indonesia. In this study, the neutrality of the CNN Indonesia media as part of Detik will be studied based on the tendency of sentiment on the omnibus law-related news. Sentiment analysis is used to examine the trend of opinion on news headlines. In conducting sentiment analysis, a method that supports classification is needed. The classification method that will be used in this research is the Support Vector Machine (SVM). There is an imbalance of data in the three categories of sentiment so that the Synthetic Minority Oversampling Technique (SMOTE) method is used to overcome this imbalance. The omnibus law tends to be reported neutrally by CNNIndonesia.com site. The one vs all method has a better classification result than the one vs one method. The application of SMOTE only gives slightly better results than data classification without the application of SMOTE because the imbalance in the data is not too extreme. Modeling using the one vs all method with SMOTE and distribution of data 90% train data 10% test data gives the best classification results with a macro average f1-score of 60,33%.
Penggerombolan Kabupaten/Kota di Indonesia Berdasarkan Indikator Indeks Pembangunan Manusia Menggunakan Metode K-Means dan Fuzzy C-Means . Hanniva; Anang Kurnia; Septian Rahardiantoro; Ahmad Ansori Mattjik
Xplore: Journal of Statistics Vol. 11 No. 1 (2022)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (973.285 KB) | DOI: 10.29244/xplore.v11i1.855

Abstract

The achievement of the human development index in Indonesia differs between regions with striking gaps occurring in the western and eastern parts of Indonesia. This difference in achievement can be seen more clearly by grouping regencies/municipalities in Indonesia based on the four indicators of the human development index. With this aim, this study uses the k-means and fuzzy c-means methods to determine the optimal cluster size with two distance approaches, namely the Euclidean and Manhattan distances on the human development index indicators data in 2020. In addition, this study also seeks to identify the distribution of regencies/municipalities based on the characteristics of the human development index indicators in the clustering result. The result is that the best distance measure is Euclidean distance with optimal cluster size is four for k-means and six for fuzzy c-means. In addition, the clustering results obtained by the k-means method are more optimal than the fuzzy c-means because the evaluation value is better. In general, the four clusters formed were in accordance with the grouping carried out by BPS with the percentage of conformity reaching 66,54%. In summary, most regencies/municipalities on the Island of Sumatera, Java, Borneo and Sulawesi have higher life expectancy and percapita expenditure than many regencies/municipalities in the Nusa Tenggara Islands (besides Bali), Moluccas and Papua. Very high achievement for each HDI indicators is dominated by the capital city of each province with unfavorable conditions occurring in most regencies/municipalities in Papua Province.
Pendekatan Metode CHAID dan Regresi Logistik dalam Menganalisis Faktor Berpengaruh pada Kejadian Stunting di Provinsi Jawa Barat Fitri Dewi Shyntia; Anang Kurnia; Gerry Alfa Dito
Xplore: Journal of Statistics Vol. 11 No. 1 (2022)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (886.259 KB) | DOI: 10.29244/xplore.v11i1.857

Abstract

Stunting is a chronic nutritional disorder characterized by short or very short height compared to the average child of his age. Data on the prevalence of stunting under five collected by the World Health Organization (WHO) in 2018 stated that Indonesia was the third-highest contributor to stunting in the South-East Asia Regional (SEAR) after Timor Leste and India. Indonesia's national stunting prevalence is 29,6%. West Java Province has the 12th the highest prevalence in Indonesia is one of the priority areas in stunting management, with the stunting prevalence rate most similar to the Indonesian national stunting prevalence of 29,2%. This study aims to examine the variables that are indicated to affect the incidence of stunting in children aged 0-59 months based on data obtained from the 2018 Basic Health Research (Riskesdas). Eighteen variables are categorized into child characteristics, nutritional fulfillment, socio-demographic, socialeconomic, and environmental characteristics. The analysis was performed using the logistic regression method and the Chi-Square Automatic Interaction Detection (CHAID) method. The analysis results show that the probability of stunting will increase significantly in children under five with several criteria. These Criteria are mothers with low education, sex of male toddlers, toddlers who do not carry out immunizations, toddlers who are not given additional food (PMT), and infants with households that have a safe place to eat and the disposal of wastewater from the kitchen is not suitable.
Penerapan Binary Particle Swarm Optimization Support Vector Machine untuk Klasifikasi Komentar Cyberbullying di Instagram Dewi Fortuna; Itasia Dina Sulvianti; Gerry Alfa Dito
Xplore: Journal of Statistics Vol. 11 No. 1 (2022)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (612.942 KB) | DOI: 10.29244/xplore.v11i1.859

Abstract

Freedom of speech on social media is sometimes inappropriate with the ethics of communicating and has led to cyberbullying. Instagram is the most commonly used social media in cyberbullying. Cyberbullying needs to be minimized because it has many adverse effects. One way that can be done is by identifying cyberbullying comments so those comments can be deleted automatically. The method used in this study is text classification using Support Vector Machine (SVM) algorithm with the application of Binary Particle Swarm Optimization (BPSO) optimization method as features selection. The study aims to build a cyberbullying comments classification model and compare the classification model performance with and without the application of features selection. The experimental results showed that modeling with SVM produces a reasonably accurate classification performance over 72% for all classification performance on each C. The application of BPSO for features selection can improve classification performance by increasing accuracy and specificity. However, the model without features selection on C = 0,1 is chosen in this study case because it has the highest sensitivity with good accuracy and specificity that can detect cyberbullying comments more accurately.

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