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All Journal ILKOM Jurnal Ilmiah Jurnal SITECH : Sistem Informasi dan Teknologi Jambura Journal of Electrical and Electronics Engineering Jurnal Nasional Cosphi Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer (BALOK) Jurnal Pengabdian Pada Masyarakat Eldimas
Rezqiwati Ishak
Universitas Ichsan Gorontalo
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Social Sciences

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Clustering Tingkat Ekonomi Mahasiswa Calon Penerima Kartu Indonesia Pintar (KIP) Kuliah Metode K-Means Maimunsuyatni Sompa; Rezqiwati Ishak
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 1 No 2 (2022): Edisi November (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (566.483 KB) | DOI: 10.37195/balok.v1i2.175

Abstract

ABSTRACTThe Smart Indonesia Card (KIP) for Higher Education by the government is under the auspices of theMinistry of Education and Culture. The Smart Indonesia Card (KIP) for Higher Education aims to helpprovide tuition assistance, especially for poor students to continue their studies. It prevents children fromdropping out of education. Universitas Ichsan Gorontalo is one of the private universities granted a quotaof the Smart Indonesia Card (KIP) for Higher Education. The limited number of student admissions (quota)of the Smart Indonesia Card (KIP) for Higher Education requires special attention in determining the rightstudents as recipients on target to get the number of quotas that are not commensurate with the number ofapplicants. In seeing that, clusters are carried out based on the economic level of students to get a groupof students prioritized to get the Smart Indonesia Card (KIP) for Higher Education. The K-Means methodgets clustering results using the Elbow technique, namely 5 clusters. The results of clustering for eachcluster indicate that Cluster 1 is a group of students with medium economic level and taken the secondpriority for recipients of assistance. Cluster 2 is a group of students with low economic levels and becomesthe first priority of recipients of assistance. Cluster 3 is a group of students with middle to high economiclevels and becomes the third priority for recipients of assistance. Cluster 4 is a group of students withmiddle economic level and become the fourth priority for recipients of assistance. Cluster 5 is a group ofstudents with middle to upper economic levels and is the fifth priority for recipients of assistance.Keywords: The Smart Indonesia Card (KIP) for Higher Education, Clustering, Elbow, K-Means
Analisis Sentimen Opini Publik Pengguna Twitter Terhadap Kenaikan Harga BBM Menggunakan Algoritma Naïve Bayes Rahmad Harun; Rezqiwati Ishak; Sudirman Panna
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 2 No 1 (2023): Edisi Mei 2023
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37195/balok.v2i1.414

Abstract

Fuel oil is needed as a support in life. Local fuel must be adjusted to international fuel prices so that the country's fiscal sustainability remains safe and not threatened. This price adjustment is carried out by the government as an effort to optimize the use and supply of fuel and to overcome the occurrence of a fuel crisis in the future. On the Twitter platform, the discussion about the fuel price increase even has become a trending topic due to the number of tweets discussing the issue. The number of opinions about the fuel price increase makes it difficult to determine the sentiment of the tweet manually. Therefore, sentiment analysis is needed that can classify the tweet whether it tends to be positive or negative. In this case, this analysis is mediated by the Naïve Bayes algorithm to classify the problem. Based on the sentiment analysis made, it can be seen that the Naïve Bayes method or algorithm can analyze tweets with good results. The accuracy generated in this sentiment analysis is 85% with a division of 80% training data and 20% test data. With the acquisition of these accuracy results, it can be said that the proposed algorithm has a fairly good diagnostic level. Keywords: sentiment analysis, Twitter, fuel oil, Naïve Bayes
Penerapan XGBoost untuk Seleksi Atribut pada K-Means dalam Clustering Penerima KIP Kuliah Amiruddin Bengnga; Rezqiwati Ishak
Jambura Journal of Electrical and Electronics Engineering Vol 5, No 2 (2023): Juli - Desember 2023
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v5i2.20253

Abstract

Pada proses clustering prioritas penerima bantuan Kartu Indonesia Pintar Kuliah dengan algoritma K-Means ada beberapa masalah yang muncul yaitu masalah seleksi atribut yang penting dan penentuan nilai K yang optimum sehingga membuat proses clustering tidak maksimal dan tidak ideal. Masalah pemilihan atribut yang penting akan diselesaikan dengan menggunakan algoritma XGBoost yang terbukti dapat digunakan untuk memecahkan masalah seperti pada proses clustering prioritas penerima bantuan KIP Kuliah. Hasil penelitian menunjukkan bahwa algoritma XGBoost dapat menentukan 3 (tiga) atribut yang paling penting yaitu Pekerjaan Ayah, Penghasilan Ibu dan Luas Bangunan dari 12 (dua belas) atribut yang ada yaitu Pekerjaan Ayah, Pekerjaan Ibu, Penghasilan Ayah, Penghasilan Ibu, Jumlah Tanggungan, Kepemilikan Rumah, Sumber Listrik, Luas Tanah, Luas Bangunan, Sumber Air, MCK, Prestasi dan metode Elbow terbukti dapat menentukan nilai K yang optimum yaitu nilai K=4. Berdasarkan penggunaan 3 (tiga) atribut terbaik dan nilai K=4 sebagai nilai K optimum berhasil didapatkan clustering yang paling maksimal dan ideal dengan nilai index terkecil yaitu 0.819 dengan menggunakan metode pengujian Davies-Bouldin Index.In the process of clustering the priority of the recipient Indonesian smart school cards with the K-Means algorithm, there are several problems that arise, namely the problem of selecting important attributes and determining the optimal value of K, so that the process is not maximum and is not ideal. Important attribute selection problems will be solved using proven XGBoost algorithm that can be used to solve problems such as in the process of clustering the priority of recipients of school KIP assistance. The results of the research showed that the XGBoost algorithm can determine the 3 (three) most important attributes, namely Father’s Work, Mother’s Production and Building Size from the 12 (twelve) attributs that exist: Father's Job, Mothers’ Work, Fathers’ Income, Mothers’ Revenue, Number of Dependants, Home Ownership, Electrical Resources, Land Area, Building Area, Water Resource, MCK, Performance and Elbow Method proved to determine the optimal K value of K=4. Based on the use of the 3 (three) best attributes and the value of K = 4 as the optimal K value, the maximum and ideal clustering with the smallest index value is 0.819 using the Davies-Bouldin Index test method.
Pengolahan Buah Aren Menjadi Produk Kolang-Kaling di Desa Kopi Kecamatan Bulango Utara Kabupaten Bone Bolango Rezqiwati Ishak; Amiruddin Amiruddin; Swastiani Dunggio; Syahrir Abdussamad
ELDIMAS: Jurnal Pengabdian Pada Masyarakat Vol 1 No 1 (2023): Mei - Oktober 2023
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/ejppm.v1i1.3

Abstract

The aren tree is a type of palm that has high economic value and is widely distributed in Indonesia, especially in Bone Bolango Regency, Gorontalo Province. All parts of the palm tree from the leaves to the roots can be used. The superior products of aren as a source of food and energy include brown sugar, ant sugar, fresh sap, and fruit and are used for various handicraft products and building materials. The purpose of implementing PKM in the coffee village is to take advantage of the village's potential to develop the community's economy by making processed palm fruit into processed fruit and fro. The method used in this is assisting MSMEs so that they can develop according to technological developments. The results achieved in this PKM are processed palm fruit products in the form of fro which are already in packaging so that they can support the validity period or expiration date of processed palm fruit products.
Penerapan Algoritma Naive Bayes Classifier Untuk Klasifikasi Judul Skripsi Berdasarkan Konsentrasi Dania, Salmin Dania; Rezqiwati Ishak; Hastuti Dalai
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 3 No 1 (2024): Mei 2024
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37195/balok.v3i1.741

Abstract

Abstract The application of the Naive Bayes Classifier algorithm to classify thesis titles based on concentration is research that aims to develop a classification system for thesis titles based on concentration using the Naive Bayes Classifier algorithm. This classification system helps students determine the thesis concentration that suits their interests and abilities. This research uses thesis title data from the Informatics Engineering Department, Faculty of Computer Science, Universitas Ichsan Gorontal.  It employs data attributes in the form of thesis title and concentration. The data are cleaned and preprocessed before being used for algorithm training and testing. The implementation of the Naive Bayes Classifier algorithm is through the Python programming language. The research results show that the Naive Bayes Classifier algorithm can classify thesis titles with an accuracy of 80% in the model evaluation process using the Confusion Matrix. The results indicate that the Naive Bayes Classifier algorithm is an effective alternative for classifying thesis titles based on concentration. Keywords: classification, thesis title, concentration, Python, Confusion Matrix, Naive Bayes Classifier
Optimization of K-Means Attribute Selection Using Correlation Matrix in Patient Disease Clustering Bengnga, Amiruddin; Ishak, Rezqiwati
Jambura Journal of Electrical and Electronics Engineering Vol 7, No 2 (2025): Juli - Desember 2025
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v7i2.28010

Abstract

Patient health is a critical element in public health systems, where grouping disease data can facilitate risk identification and more efficient treatment planning. However,  conventional clustering methods  such as K-Means often have difficulty in separating clusters optimally, especially when the attributes used are irrelevant or redundant. This study aims to optimize  the clustering process  of patient health data by applying attribute selection using Correlation Matrix and Heatmap in the K-Means algorithm. The method used involves normalizing the data with a StandardScaler and determining the optimal number of clusters through  the Elbow Method, which results in three  optimal clusters. Attribute selection is carried out to reduce redundancy, leaving important features such as age, height, and body mass index (BMI). The results of the analysis showed that attribute selection significantly improved clustering performance, with the Silhouette Score increasing from 0.20 to 0.54 and  the Davies-Bouldin Index (DBI) decreasing from 1.60 to 0.63. Visualization of clustering results  using Principal Component Analysis (PCA) shows a clearer separation between clusters, reflecting different patient characteristics. These findings confirm the importance of attribute selection in  the clustering process  to achieve more optimal results that can help in understanding patient health patterns and designing more appropriate interventions.Kesehatan pasien merupakan elemen penting dalam sistem kesehatan masyarakat, di mana pengelompokan data penyakit dapat memfasilitasi identifikasi risiko dan perencanaan perawatan yang lebih efisien. Namun metode clustering konvensional seperti K-Means sering mengalami kesulitan dalam memisahkan cluster secara optimal, terutama ketika atribut yang digunakan tidak relevan atau berlebihan. Penelitian ini bertujuan untuk mengoptimalkan proses clustering data kesehatan pasien dengan menerapkan seleksi atribut menggunakan Correlation Matrix dan Heatmap dalam algoritma K-Means. Metode yang digunakan melibatkan normalisasi data dengan StandardScaler dan penentuan jumlah cluster optimal melalui Elbow Method, yang menghasilkan tiga cluster optimal. Seleksi atribut dilakukan untuk mengurangi redundansi, menyisakan fitur-fitur penting seperti umur, tinggi badan, dan indeks massa tubuh (IMT). Hasil analisis menunjukkan bahwa seleksi atribut secara signifikan meningkatkan performa clustering, dengan Silhouette Score meningkat dari 0,20 menjadi 0,54 dan Davies-Bouldin Index (DBI) menurun dari 1,60 menjadi 0,63. Visualisasi hasil clustering menggunakan Principal Component Analysis (PCA) menunjukkan pemisahan yang lebih jelas antar cluster, mencerminkan karakteristik pasien yang berbeda. Temuan ini menegaskan pentingnya seleksi atribut dalam proses clustering untuk mencapai hasil yang lebih optimal yang dapat membantu dalam memahami pola kesehatan pasien dan merancang intervensi yang lebih tepat.  
Clustering Prestasi Akademik Lulusan Menggunakan Metode K-Means Ishak, Rezqiwati; Bengnga, Amiruddin
Jambura Journal of Electrical and Electronics Engineering Vol 6, No 1 (2024): Januari-Juni 2024
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v6i1.23967

Abstract

Prestasi akademik merupakan salah satu indikator penting untuk mengukur keberhasilan seorang mahasiswa dalam menyelesaikan studinya di perguruan tinggi. Prestasi ini dapat dilihat dari berbagai aspek, seperti lama studi dan Indeks Prestasi Kumulatif (IPK). Analisis ini digunakan untuk meningkatkan kualitas Pendidikan pada Perguruan Tinggi itu sendiri, serta untuk membantu Mahasiswa dalam mencapai prestasi yang optimal. Penelitian ini bertujuan untuk melakukan clustering prestasi akademik lulusan pada Universitas Ichsan Gorontalo untuk Tahun Akademik 2023/2024 semester Ganjil dengan menerapkan metode K-Means. Jumlah dataset lulusan yang digunakan sebanyak 240 data. Analisis clustering dilakukan berdasarkan atribut lama studi, umur, dan Indeks Prestasi Kumulatif (IPK). Hasil penelitian ini menunjukkan adanya 3 (tiga) cluster utama. Cluster 1 (satu) merupakan kelompok lulusan dengan prestasi akademik cukup baik, terdiri dari 56 lulusan. Cluster 2 (dua) menggambarkan kelompok lulusan dengan prestasi akademik sangat baik, terdiri dari 138 lulusan. Sementara itu, Cluster 3 (tiga) menunjukkan kelompok lulusan dengan prestasi akademik kurang baik jika dilihat dari lama studi, terdiri dari 45 lulusan. Pemilihan jumlah cluster sebanyak 3 didasarkan pada hasil perhitungan teknik Elbow dan evaluasi Davies-Bouldin Index yang memberikan nilai terkecil yakni  0,79 sehingga hasil clustering masuk kategori baik karena nilai DBInya di bawah 1.Academic achievement is one of the important indicators to measure a student's success in completing their studies at the university. This achievement can be observed from various aspects, such as the duration of study and the Cumulative Grade Point Average (GPA). This analysis is used to improve the quality of education at the university itself and to assist students in achieving optimal performance. This research aims to cluster the academic achievements of graduates at Ichsan Gorontalo University for the Academic Year 2023/2024 Odd Semester using the K-Means method. The number of graduate datasets used is 240. The clustering analysis is based on attributes such as the duration of study, age, and Cumulative Grade Point Average (GPA). The results of this study indicate the existence of 3 main clusters. Cluster 1 represents graduates with fairly good academic achievements, consisting of 56 graduates. Cluster 2 describes a group of graduates with excellent academic achievements, totaling 138 graduates. Meanwhile, Cluster 3 indicates a group of graduates with less satisfactory academic achievements when considering the duration of study, consisting of 45 graduates. The selection of 3 clusters is based on the results of the Elbow technique calculation and the evaluation of the Davies-Bouldin Index, which gives the smallest value of 0.79. Therefore, the clustering results are considered good because the DBI value is below 1.
Optimization of K-Means in Disease Clustering of Pregnant Women Using Random Forest Ishak, Rezqiwati; Nurmawanti, Nurmawanti; Bengnga, Amiruddin
Jambura Journal of Electrical and Electronics Engineering Vol 7, No 1 (2025): Januari - Juni 2025
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v7i1.28374

Abstract

Pregnant women's health is an important aspect of the public health system, where grouping disease data can help in risk identification and better treatment planning. However, traditional clustering methods such as K-Means often face challenges in optimal separation between clusters, especially when the attributes used are irrelevant. This study aims to optimize the K-Means method in disease clustering in pregnant women by applying Random Forest-based attribute selection. Of the six available attributes (age, weight, height, gestational age, systole, and diastole), the three main attributes namely systole, diastole, and gestational age were selected based on the Importance Score from Random Forest. The test results showed that the use of these three attributes increased the Silhouette Score by 0.21 (from 0.23 to 0.44), indicating better cluster separation, and lowered the Davies-Bouldin Index by 0.69 (from 1.50 to 0.81), indicating a more compact and well-separated cluster. Clustering visualization using Principal Component Analysis (PCA) supports these results. In addition, the calculation of the Elbow method shows the optimal number of clusters at k=3, reinforcing the conclusion that the selection of the right attributes and the number of clusters improves the quality of clustering. Overall, this study proves that the selection of Random Forest-based features is able to optimize the K-Means method in disease clustering in pregnant women, which is expected to improve the effectiveness of diagnosis and treatment planning.Kesehatan ibu hamil merupakan aspek penting dalam sistem kesehatan masyarakat, di mana pengelompokan data penyakit dapat membantu dalam identifikasi risiko dan perencanaan perawatan yang lebih baik. Namun, metode clustering tradisional seperti K-Means sering kali menghadapi tantangan dalam pemisahan yang optimal antar cluster, terutama ketika atribut yang digunakan tidak relevan. Penelitian ini bertujuan untuk mengoptimalkan metode K-Means dalam clustering penyakit pada ibu hamil dengan menerapkan seleksi atribut berbasis Random Forest. Dari enam atribut yang tersedia (usia, berat badan, tinggi badan, usia kehamilan, sistole, dan diastole), tiga atribut utama yaitu sistole, diastole, dan usia kehamilan dipilih berdasarkan Importance Score dari Random Forest. Hasil pengujian menunjukkan bahwa penggunaan tiga atribut ini meningkatkan Silhouette Score sebesar 0,21 (dari 0,23 menjadi 0,44), yang mengindikasikan pemisahan cluster yang lebih baik, serta menurunkan Davies-Bouldin Index sebesar 0,69 (dari 1,50 menjadi 0,81), menunjukkan cluster yang lebih kompak dan terpisah dengan baik. Visualisasi clustering menggunakan Principal Component Analysis (PCA) mendukung hasil ini. Selain itu, perhitungan metode Elbow menunjukkan jumlah cluster optimal pada k=3, memperkuat kesimpulan bahwa pemilihan atribut dan jumlah cluster yang tepat meningkatkan kualitas clustering. Secara keseluruhan, penelitian ini membuktikan bahwa seleksi fitur berbasis Random Forest mampu mengoptimalkan metode K-Means dalam clustering penyakit pada ibu hamil, yang diharapkan dapat meningkatkan efektivitas diagnosis dan perencanaan perawatan.
Co-Authors Amiruddin Bengnga Asmaul Husnah Nasrullah Ayu Azhari Basahona Bengnga, Amiruddin Dania, Salmin Dania Hastuti Dalai Maimunsuyatni Sompa Mursalin, Nurhayati Nurhayati Mursalin nurmawanti nurmawanti, nurmawanti Rahmad Harun Sudirman S. Panna Swastiani Dunggio Syahrir Abdussamad