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All Journal Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer BAREKENG: Jurnal Ilmu Matematika dan Terapan SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Variance : Journal of Statistics and Its Applications Journal of Mathematics Education and Science Jurnal Pengabdian Masyarakat Indonesia MATHunesa: Jurnal Ilmiah Matematika Journal of Social Outreach Journal of Research Applications in Community Services Basis : Jurnal Ilmiah Matematika Teknikom: Teknologi Informasi, Ilmu Komputer dan Manajemen Eksponensial Georeference: Jurnal Kajian Ilmu dan Pembelajaran Geografi JMT (Jurnal Matematika dan Terapan) MILANG Journal of Mathematics and Its Applications

Fidia Deny Tisna Amijaya

Program Studi Matematika, Jurusan Matematika, FMIPA, Universitas Mulawarman

Author-ID : 6295497

Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Analisis Cluster Non-Hirarki Dengan Menggunakan Metode K-Modes pada Mahasiswa Program Studi Statistika Angkatan 2015 FMIPA Universitas Mulawarman Nur Amah; Sri Wahyuningsih; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 8 No 1 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (442.182 KB)

Cluster analysis is a technique that used to categorize or classify object into clusters or group which is relatively homogeneous. This research aims to know the number of the best cluster used in the selection of Statistics major using K-Modes Cluster, which variable as the best center of cluster & the most optimum, and also comparison of the cluster based on the Davies-Bouldin Index (DBI) which is derived in each cluster are 2 clusters, 3 clusters, and 4 clusters. Steps in this research is descriptive analysis, validity and reliability of questionnaire, determine the number of clusters, compute the dissimiliarity distance, calculate the cluster validation and interpretate the result of the best cluster. Selection of the best cluster use the smallest value comparison. The smallest of the two clusters are 0,599. The center (centroid) of clusters variables which is the best optimum using K-Modes with two clusters are for the first centroid is the first choice of major, SNMPTN, IPK satisfactory, study routines for 4 times a week, and the average length of study is between 60 minutes to 120 minutes per day.; for the second centroid is the first choice of study program, SNMPTN, IPK is very satisfied, study routines for 6 times a week, and the average length of study is less than or equal to 60 minutes per day. The final results showed that the best cluster produced is two clusters where cluster 1 consisted of 37 students and cluster 2 consisted of 8 students.

Peramalan Harga Minyak Mentah Menggunakan Model Autoregressive Integrated Moving Average Neural Network (ARIMA-NN) Laila Nur Qamara; Sri Wahyuningsih; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 10 No 2 (2019): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (647.856 KB)

Crude oil prices can affect the production and consumption of a country. Crude oil prices are set every month and semester by Indonesian Crude Oil Price(ICP). Bontang Return Condensate (BRC) is one of crude oil type in Indonesia. Forecasting is an expectation of a request or thing that will come based on several forecasting variables.In this study, data was used in July 2010-December 2017. The purpose of this study was to determine the best model of Indonesian crude oil price data for the BRC type and the forecasting results. The model used in this study is the ARIMA-NN model which is a combination of ARIMA model and Neural Network (NN) model. The best ARIMA-NN model has ARIMA (2,1,0) and NN components with 2 inputs and 2 neurons in the hidden layer. The NN model is a Feed Forward Neural Network (FFNN) model with backpropagation algorithm. The results of the forecasting of the BRC Indonesia crude oil price for January-December 2018 are around the value of 60 USD / Barrel.

Aplikasi Metode Naive Bayes dalam Prediksi Risiko Penyakit Jantung M. Sabransyah; Yuki Novia Nasution; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 8 No 2 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (654.691 KB)

Classification is an activity for assessing object data which include it the data into particular class among any number of classes available. Naive Bayes is classification with probability method. This research examines the use of naive Bayes method for a heart disease risk prediction application. In this research, it will be classified a person who have the risk of heart disease by using the data of patient in RSUD AWS during November and December 2016 the sample case is 47 years old male object, has cholesterol level of 198 mg/dL, has blood pressure of 131 mmHg, parents having heart disease medical record, suffering diabetes Mellitus, has obesity, has high dyslipidemia. It is concluded that the object falls into "potential category" of having heart disease. The classification result that has been done, the exact accuracy was obtained with 25 tested data and got accuracy level in an amount of 80% and 50 tested data sample and got accuracy level in an amount of 78%.

Pelatihan Olimpiade Sains Nasional (OSN) Bidang Matematika untuk Siswa-Siswi SMA/MA di Kota Samarinda Desi Febriani Putri; Fidia Deny Tisna Amijaya; Wasono Wasono; Indriasri Raming; Sri Wigantono; Syaripuddin Syaripuddin; Moh. Nurul Huda; Qonita Qurrota A’yun; Hardina Sandariria; Husna Novia Ramadhanty; Korompot Naufal Fahrezi; Rabbiatul Adawiyah; Dimas Raditya Sahputra
Journal of Research Applications in Community Service Vol. 2 No. 3 (2023): Journal of Research Applications in Community Service
Publisher : Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/jarcoms.v2i3.2239

Olimpiade Sains Nasional (OSN) adalah agenda tahunan yang diselenggarakan Kementerian Pendidikan, Kebudayaan, Riset dan Teknologi yang ditujukan kepada siswa-siswi tingkat SD/MI, SMP/MTs dan SMA/MA. OSN merupakan salah satu strategi untuk meningkatkan mutu pendidikan. Melalui kompetisi sains, siswa-siswi akan memiliki jiwa kompetitif sekaligus menumbuhkan karakter siswa yang jujur, disiplin, tekun dan kreatif. Hasil Olimpiade Sains Nasional (OSN) jenjang SMA/MA tingkat Kabupaten/Kota untuk bidang matematika mengalami penurunan sebanyak 50% dari tahun 2022 ke tahun 2023. Sedangkan untuk pemenang lomba, perwakilan Provinsi Kalimantan Timur tidak ada yang memperoleh medali baik itu emas, perak maupun perunggu. Untuk itu Prodi Matematika FMIPA Universitas Mulawarman memberikan kontribusi untuk memberikan pembinaan olimpiade terutama di bidang matematika. Harapannya peserta pembinaan OSN ini mempunyai persiapan yang lebih matang sebelum mengikuti proses seleksi di tingkat sekolah, kota/kabupaten, provinsi bahkan sampai nasional, sehingga mereka mampu bersaing dengan siswa-siswi dari luar Provinsi Kalimantan Timur. Peserta kegiatan pengabdian kepada masyarakat adalah siswa-siswi SMA/MA perwakilan sekolah yang ada di Kota Samarinda. Kota Samarinda dipilih karena pertimbangan kemudahan akses yang lebih dekat. Metode yang digunakan pada pelatihan ini adalah metode ceramah, tanya jawab, latihan soal dan diskusi. Sebelum pelatihan dimulai para peserta harus mengikuti pre-test terlebih dahulu dan diakhir pelatihan peserta juga mengikuti post-test. Data tes awal dengan rata-rata sebesar 30 dan data tes akhir dengan rata-rata sebesar 55 dianalisis menggunakan Shapiro Wilk dan uji beda rata-rata Wilcoxon. Hasilnya didapatkan nilai 4,81 x 10-5 < nilai alpha (α) 0,05. Artinya terdapat perbedaan rata-rata pada data pre-test dan data post-test. Nilai rata-rata data tes awal < rata-rata data tes akhir, sehingga dapat dikatakan tim pengabdian kepada masyarakat berhasil memberikan pengalaman dan pemahaman kepada peserta pelatihan Olimpiade Sains Nasional (OSN) Bidang Matematika untuk siswa-siswi SMA/MA di Kota Samarinda.

Perbandingan Algoritma Support Vector Machine dan Naïve Bayes pada Klasifikasi Penyakit Tekanan Darah Tinggi (Studi Kasus: Klinik Polresta Samarinda) Raka Putra Pridiptama; Wasono Wasono; Fidia Deny Tisna Amijaya
Basis : Jurnal Ilmiah Matematika Vol 3 No 1 (2024): BASIS: Jurnal Ilmiah Matematika
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/basis.v3i1.1264

Klasifikasi adalah suatu proses untuk menemukan sifat-sifat yang sama dalam suatu himpunan data untuk diklasifikasikan ke dalam kelas-kelas yang berbeda. Algoritma metode klasifikasi yang digunakan dalam penelitian ini adalah support vector machine (SVM) dan naïve Bayes. Algoritma SVM adalah supervised learning yang bekerja dengan mencari hyperplane atau fungsi pemisah terbaik untuk memisahkan kelas, sedangkan naïve Bayes adalah supervised learning yang didasarkan pada asumsi kemandirian (naif) antar prediktor yang dikenal dengan teorema Bayes. Penelitian ini bertujuan untuk mengetahui model dan keakuratan algoritma SVM dan naïve Bayes dalam melakukan klasifikasi terhadap status hipertensi dari rekam medis pasien di Klinik Polresta Samarinda tahun 2022. Berdasarkan analisis akurasi pada algoritma SVM sebesar 96,67% dengan tepat mengklasifikasikan 29 dari 30 data sedangkan pada algoritma naïve Bayes sebesar 93,33% dengan tepat mengklasifikasikan 28 dari 30 data. Hasil perbandingan pengukuran akurasi dari kedua algoritma tersebut menunjukkan bahwa algoritma SVM memiliki tingkat akurasi yang lebih baik dibandingkan dengan algoritma naïve Bayes.

Perbandingan Hasil Analisis Cluster Dengan Menggunakan Metode Average Linkage Dan Metode Ward Imasdiani, Imasdiani; Purnamasari, Ika; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol 13 No 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Hierarchical cluster analysis is an analysis used to classify data based on its characteristics. The average linkage method and the Ward method are methods of hierarchical cluster analysis. Grouping data from various aspects, one of which is poverty. This study uses poverty indicator data in East Kalimantan in 2018. The average linkage method is based on the average distance size, while the Ward method is based on the size of the distance between clusters by minimizing the number of squares. The purpose of this study was to determine the best method based on the average value of the standard deviation ratio. The results of the study using the average linkage method obtained two clusters, both the average linkage method and the Ward method both obtained two clusters. Where in the average linkage method, the first cluster consists of 7 districts / cities and the second cluster consists of 3 districts / cities. Whereas in the Ward method, the first cluster consists of 6 districts / cities and the second cluster consists of 4 districts / cities. For the best method based on the average standard deviation ratio in groups (Sw) and the standard deviation between groups (Sb), it is found that the ratio in the Ward method is smaller than the average linkage method, which is 2,681 which indicates that the average linkage method is the best method.

Perbandingan Metode Klasifikasi Naïve Bayes Dan Jaringan Saraf Tiruan Ardyanti, Hesti; Goejantoro, Rito; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol 11 No 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Classification is a technique to form a model of data that is already known to its classification group. The model that was formed will be used to classify new object. Naïve Bayes is a classification technique for predicting future probability based on past experiences with a strong assumption of independence. Artificial neural network is one of the data mining analysis tools that can be used to create data on classification. Model selection in artifial neural networks requires various factors such as the selection of optimal number of hidden neuron. This research has a goal to compare the level of classification accuracy between the Naïve Bayes method and artificial neural network on payment status of the insurance premium. The data used is insurance costumer’s data of PT AJB Bumiputera Samarinda in 2018. The result of the comparison of accuracy calculation from the two analyzes indicate that artificial neural network has a higher level of accuracy than naïve Bayes method. Classification accuracy result of Naïve Bayes is 82,76% and artificial neural network is 86,21%.

Analisis Cluster Pada Data Kategorik dan Numerik dengan Pendekatan Cluster Ensemble Lestari, Nur Aini Ayu; Hayati, Memi Nor; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol 11 No 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Cluster analysis used to process categorical and numerical data at once is Cluster Ensemble algorithm Based on Mixed Data Clustering (algCEBMDC), which is a cluster algorithm with an ensemble cluster approach. The method used for numerical data is Agglomerative Nesting (AGNES) algorithm and for categorical data is the RObust Clustering using linK (ROCK) algorithm. The best clustering method and the optimum number of clusters in the AGNES algorithm is selected based on the maximum Pseudo-F value and the minimum icdrate value. The optimum number of clusters in the ROCK algorithm is selected using the minimum value of ratio . The purpose of this study was to make a group of 179 Puskesmas in East Kalimantan on December 2017. Based on the results of the analysis, obtained 5 optimum cluster for numerical clustering with the AGNES algorithm and 2 optimum cluster for categorical clustering data with the ROCK algorithm. Final cluster for mixed data clustering obtained 2 optimum cluster at a threshold of 0.2 and 0.3 with value of ratio is . The first cluster consists of 83 Puskesmas and cluster two of 96 Puskesmas.

Penyelesaian Masalah Pemrograman Kuadratik Menggunakan Metode Beale Erlina, Erlina; Syaripuddin, Syaripuddin; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol 13 No 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v13i1.889

Quadratic programming is a special form of nonlinear programming which has the general form of the objective function in the form of a quadratic functions and the constraints in the form of linear functions. One of the methods used to solve the quadratic programming model is Beale’s method. This method is a modification of the simplex method for linear programming problems. This study aims to determine the optmal results of paddy production data in Balikpapan city using Beale’s method. The quadratic programming model was formed using the least squares method with the objective functions by selecting two types of plants,namely lowland paddy and upland paddy. Furthermore, the quadratic programming model formed that is formed is solved using Beale’s method. The data used is data on the paddy production of balikpapan city in 2005-2019. Based on the calculatios, the objective function is with the constraint functions and . After calculating using Beale’s method, the optimal result for the maximum yield of lowland paddy and upland paddy is quintals with a harvested area of hectares of lowland paddy and a harvested paddy of hectares of upland paddy.

Penerapan Algoritma K-Medoids pada Pengelompokan Wilayah Desa atau Kelurahan di Kabupaten Kutai Kartanegara Ibrahim, Rizky Nur; Hayati, Memi Nor; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol 11 No 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v11i2.658

Kutai Kartanegara Regency (Kukar) was recorded as the largest contributor to the poor population in East Kalimantan (Kaltim) Province in 2017, so that appropriate strategies are needed to solve proverty problems. The development strategy is prioritized for the regions with the largest number of poor people. Identification is conducted based on facilities, infrastructures, access, social, population and economy is provided in the Village Potential data (PODES). K-Medoids is a grouping method that uses representative objects as a central point, which can be used to find out the characteristics of a region. This research is aimed to find out the optimal cluster formed by choosing the largest value of Silhouette Coefficient (SC) from the grouping of villages / political district in Kukar Regency using PODES data in 2018. Clusters that will be formed in this research are 2 clusters, 3 clusters, 4 clusters and 5 clusters. Based on the analysis, it can be seen that the value of SC 2 cluster is 0.430, the value of SC 3 cluster is 0.174, the value of SC 4 cluster is 0.175 and the value of SC 5 cluster is 0.196. So that the largest SC or optimal cluster values obtained in the grouping of 2 clusters with a SC value of 0.430. Cluster 1 consists of 186 villages / political dsitrict and cluster 2 consists of 46 villages / political district.

Co-Authors A'yun, Qonita Qurrota Adawiyah, Rabbiatul Akbar Rizky Wardani Aminah, Esse Andri Azmul Fauzi Ardyanti, Hesti Asmaidi Asmaidi Asmaidi Auliya Rahman Ayu Wulandari A’yun, Qonita Qurrota Bayu Iswahyudi Noor Br Tarigan, Agnes Janitarian Cahyadi, Aldy Fradana Mahaputra Clemensius Arles Dala, Maria Alensia Deltin David Siahaan Desi Febriani Putri Desi Febriani Putri Dewi Erla Mahmudah Dewi Erla Mahmudah, Dewi Erla Dimas Raditya Dimas Raditya Sahputra Dimas Raditya Sahputra Dwi Indra Yunistya Dyah Arumatica Novilla Elvita, Melati Erlina Erlina Fahreza, Ilham Farha, Izzaty Fauzi, Andri Azmul Fiqri, Muhammad Dul Gunsyang, Grassella Hardina Sandariria Husna Novia Husna Novia Ramadhanty Ibrahim, Rizky Nur Ika Purnamasari Ika Purnamasari Imasdiani, Imasdiani Indriasri Raming Itsar Mangngiri Izzaty Farha Karina Putri Korompot Naufal Fahrezi Kurniawan Noor Bilal Kusrahman, Nanda Yopan Laila Nur Qamara Latifah Uswatun Khasanah Lestari, Nur Aini Ayu Lisda Ramadhani M. Sabransyah Mahmudi Mahmudi Martua Tri Januar Sinaga Meiliyani Siringoringo Melati Elvita Memi Nor Hayati Moch Nurul Huda Moh Khoridatul Huda, Moh Khoridatul Moh. Nurul Huda Muhammad Faisal Munfaati, Rafika Husnia Mushalifah, Mushalifah Mustika, Anggi Winda Nadya Rahmawati Nanang Wahyudi Neni Rahayu Nola Febriana Saputri Nur Amah Nur Aminah Pasarella, Muhammad Danil Pasia Rande Putri Pita Mutia Putri, Annisa Amalia Putri, Desi Febriani Qonita Qurrota A'yun Qonita Qurrota A’yun Rabbiatul Adawiyah Rachel Cornelia Simanjuntak Rachman, Dezty Adhe Chajannah Rachmawati, Amalia Raka Putra Pridiptama Rakhmawaty, Nurul Raming, Indriasri Ratna Dwi Christyanti, Ratna Dwi Rito Goejantoro, Rito Sahputra, Dimas Raditya Said Said, Said Sandariria, Hardina Sifriyani, Sifriyani Sri Wahyuningsih Sri Wahyuningsih Sri Wigantono Stefania Sesilia G. Witin Suciati, Rara Syaripuddin Syaripuddin Syaripuddin Syaripuddin Taqriri Kamal Mulyadi Tulzahrah, Shanaz Tumilaar, Rinancy Vika Novitasari Wasono Wasono Wasono Wasono Wasono, Wasono Welly Dona Permatasari Wigantono, Sri Yoki Novia Nasution Yuki Novia Nasution Yuki Novia Nasution Yuki Novia Nasution Yuki Novia Nasution Yuki Novia Nasution, Yuki Novia Yuliasari, Pratiwi Dwi

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