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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Transportasi Habitat Jurnal Ilmiah Teknik Industri Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Gantang JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JET (Journal of Electrical Technology) BAREKENG: Jurnal Ilmu Matematika dan Terapan Jiko (Jurnal Informatika dan komputer) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jambura Journal of Mathematics Jurnal Ilmiah Ilmu dan Teknologi Rekayasa MOTIVECTION : Journal of Mechanical, Electrical and Industrial Engineering MEANS (Media Informasi Analisa dan Sistem) Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Abdimas Bina Bangsa IJCOSIN : Indonesian Journal of Community Service and Innovation Jurnal Ilmiah Mahasiswa teknik eProceedings of Engineering Jurnal INFOTEL Abdi Masyarakat UIKA Ngabdimas Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi Integrasi: Jurnal Ilmiah Teknik Industri
Ridho Ananda
Telkom University Purwokerto
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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SILHOUETTE DENSITY CANOPY K-MEANS FOR MAPPING THE QUALITY OF EDUCATION BASED ON THE RESULTS OF THE 2019 NATIONAL EXAM IN BANYUMAS REGENCY Ananda, Ridho
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8375

Abstract

Mapping the quality of education units is needed by stakeholders in education. To do this, clustering is considered as one of the methods that can be applied. K-means is a popular algorithm in the clustering method. In its process, K-means requires initial centroids randomly. Some scientists have proposed algorithms to determine the number of initial centroids and their location, one of which is density canopy (DC) algorithm. In the process, DC forms centroids based on the number of neighbors. This study proposes additional Silhouette criteria for DC algorithm. The development of DC is called Silhouette Density Canopy (SDC). SDC K-means (SDCKM) is applied to map the quality of education units and is compared with DC K-means (DCKM) and K-means (KM). The data used in this study originated from the 2019 senior high school national examination dataset of natural science, social science, and language programs in the Banyumas Regency. The results of the study revealed that clustering through SDKCM was better than DCKM and KM, but it took more time in the process. Mapping the quality of education with SDKCM formed three clusters for social science and natural science datasets and two clusters for language program dataset. Schools included in cluster 2 had a better quality of education compared to other schools.
Analysis of Postponement Practice in Cement Supply Chain: A Case Study Romadlon, Fauzan; Kurniawan, Hadi; Ananda, Ridho
Jurnal Ilmiah Teknik Industri Vol. 19, No. 02, December 2020
Publisher : Department of Industrial Engineering Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/jiti.v19i2.11343

Abstract

This research focuses on assessing implementing postponement in a cement industry especially for OPC (Ordinary Portland Cement) in Indonesia. The company offers three postponement points; Ciawi and Kampung Rambutan, and Jatiwarna. This research has been done with observational method to map customer condition. Moreover, implementing linear programming model is used to gain optimal solution for allocated truck. The results are, highest proportion of OPC customer is in Jakarta, followed Karawang, and Bogor. The result from linear programming model simulation, Ciawi point is assigned to serve Jakarta and Bogor’ customers, Kampung Rambutan point is assigned for Jakarta and Kawarang’ customers, and Jatiwarna point is prepared for Jakarta, Karawang, Bekasi, Lampung, Pekalongan, Tangerang, and Serang’ customers. So, implementing postponement can increase customer satisfaction without adding higher cost such as investment cost. The cement industry encounter effectiveness and responsiveness even tough, some problem shall be taken such as double administration process (manual and computerized).
Hierarchical and K-means Clustering in the Line Drawing Data Shape Using Procrustes Analysis Ridho Ananda; Agi Prasetiadi
JOIV : International Journal on Informatics Visualization Vol 5, No 3 (2021)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.3.532

Abstract

One of the problems in the clustering process is that the objects under inquiry are multivariate measures containing geometrical information that requires shape clustering. Because Procrustes is a technique to obtaining the similarity measure of two shapes, it can become the solution. Therefore, this paper tried to use Procrustes as the main process in the clustering method. Several algorithms proposed for the shape clustering process using Procrustes were namely hierarchical the goodness-of-fit of Procrustes (HGoFP), k-means the goodness-of-fit of Procrustes (KMGoFP), hierarchical ordinary Procrustes analysis (HOPA), and k-means ordinary Procrustes analysis (KMOPA). Those algorithms were evaluated using Rand index, Jaccard index, F-measure, and Purity. Data used was the line drawing dataset that consisted of 180 drawings classified into six clusters. The results showed that the HGoFP, KMGoFP, HOPA and KMOPA algorithms were good enough in Rand index, F-measure, and Purity with 0.697 as a minimum value. Meanwhile, the good clustering results in the Jaccard index were only the HGoFP, KMGoFP, and HOPA algorithms with 0.561 as a minimum value. KMGoFP has the worst result in the Jaccard index that is about 0.300. In the time complexity, the fastest algorithm is the HGoFP algorithm; the time complexity is 4.733. Based on the results, the algorithms proposed in this paper particularly deserve to be proposed as new algorithms to cluster the objects in the line drawing dataset. Then, the HGoFP is suggested clustering the objects in the dataset used.
A COMPARISON OF CLUSTERING BY IMPUTATION AND SPECIAL CLUSTERING ALGORITHMS ON THE REAL INCOMPLETE DATA Ridho Ananda; Atika Ratna Dewi; Nurlaili Nurlaili
Jurnal Ilmu Komputer dan Informasi Vol 13, No 2 (2020): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v13i2.818

Abstract

The existence of missing values will really inhibit process of clustering. To overcome it, some of scientists have found several solutions. Both of them are imputation and special clustering algorithms. This paper compared the results of clustering by using them in incomplete data. K-means algorithms was utilized in the imputation data. The algorithms used were distribution free multiple imputation (DFMI), Gabriel eigen (GE), expectation maximization-singular value decomposition (EM-SVD), biplot imputation (BI), four algorithms of modified fuzzy c-means (FCM), k-means soft constraints (KSC), distance estimation strategy fuzzy c-means (DESFCM), k-means soft constraints imputed-observed (KSC-IO). The data used were the 2018 environmental performance index (EPI) and the simulation data. The optimal clustering on the 2018 EPI data would be chosen based on Silhouette index, where previously, it had been tested its capability in simulation dataset. The results showed that Silhouette index have the good capability to validate the clustering results in the incomplete dataset and the optimal clustering in the 2018 EPI dataset was obtained by k-means using BI where the silhouette index and time complexity were 0.613 and 0.063 respectively. Based on the results, k-means by using BI is suggested processing clustering analysis in the 2018 EPI dataset.
Analysis of Postponement Practice in Cement Supply Chain: A Case Study Fauzan Romadlon; Hadi Kurniawan; Ridho Ananda
Jurnal Ilmiah Teknik Industri Vol. 19, No. 02, December 2020
Publisher : Department of Industrial Engineering Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/jiti.v19i2.11343

Abstract

This research focuses on assessing implementing postponement in a cement industry especially for OPC (Ordinary Portland Cement) in Indonesia. The company offers three postponement points; Ciawi and Kampung Rambutan, and Jatiwarna. This research has been done with observational method to map customer condition. Moreover, implementing linear programming model is used to gain optimal solution for allocated truck. The results are, highest proportion of OPC customer is in Jakarta, followed Karawang, and Bogor. The result from linear programming model simulation, Ciawi point is assigned to serve Jakarta and Bogor’ customers, Kampung Rambutan point is assigned for Jakarta and Kawarang’ customers, and Jatiwarna point is prepared for Jakarta, Karawang, Bekasi, Lampung, Pekalongan, Tangerang, and Serang’ customers. So, implementing postponement can increase customer satisfaction without adding higher cost such as investment cost. The cement industry encounter effectiveness and responsiveness even tough, some problem shall be taken such as double administration process (manual and computerized).
Silhouette Density Canopy K-Means for Mapping the Quality of Education Based on the Results of the 2019 National Exam in Banyumas Regency Ridho Ananda
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8375

Abstract

Mapping the quality of education units is needed by stakeholders in education. To do this, clustering is considered as one of the methods that can be applied. K-means is a popular algorithm in the clustering method. In its process, K-means requires initial centroids randomly. Some scientists have proposed algorithms to determine the number of initial centroids and their location, one of which is density canopy (DC) algorithm. In the process, DC forms centroids based on the number of neighbors. This study proposes additional Silhouette criteria for DC algorithm. The development of DC is called Silhouette Density Canopy (SDC). SDC K-means (SDCKM) is applied to map the quality of education units and is compared with DC K-means (DCKM) and K-means (KM). The data used in this study originated from the 2019 senior high school national examination dataset of natural science, social science, and language programs in the Banyumas Regency. The results of the study revealed that clustering through SDKCM was better than DCKM and KM, but it took more time in the process. Mapping the quality of education with SDKCM formed three clusters for social science and natural science datasets and two clusters for language program dataset. Schools included in cluster 2 had a better quality of education compared to other schools.
Sistem Rekomendasi Pemilihan Peminatan Menggunakan Density Canopy K-Means Ridho Ananda; Muhammad Zidny Naf’an; Amalia Beladinna Arifa; Auliya Burhanuddin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 1 (2020): Februari 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (623.312 KB) | DOI: 10.29207/resti.v4i1.1531

Abstract

The carelessly selection of specialization course leaves some students with difficulty. Therefore, it is needed a recommendation system to solve it. Several approaches could be used to build the system, one of them was K-Means. K-Means required the number of initial centroid at random, so its result was not yet optimal. To determine the optimal initial centroid, Density Canopy (DC) algorithms had been proposed. In this research, DC and K-Means (DCKM) was implemented to build the recommendation system in the problem. The alpha criterion was also proposed to improve the performance of DCKM. The academic quality dataset in the 2018 informatics programs students of ITTP was used. There were three main stages in the system, namely determination of the weight of the course in dataset, implementation of DCKM, and determination of specialization recommendations. The results showed that the system by using DCKM has good quality based on the Silhouette results (at least 0.655). The system also used standar valuation scale in ITTP and silhouette index in the process of system. The results showed that 176 (65.91%) students were recommended in IT specialization, 25 (9.36%) students were recommended in MM specialization and 66 (24.7%) students were recommended in SC specialization.
Penentuan Centroid Awal K-means pada Proses Clustering Data Evaluasi Pengajaran Dosen Ridho Ananda; Achmad Zaki Yamani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 3 (2020): Juni 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (439.837 KB) | DOI: 10.29207/resti.v4i3.1896

Abstract

Decision making about microteaching for lecturers in ITTP with the low teaching quality is only based on three lowest order from teaching values. Consequently, the decision is imprecise, because there is possibility that the lecturers are not three. To get the precise quantity, an analysis is needed to classify the lecturers based on their teaching values. Clustering is one of analyses that can be solution where the popular clustering algorithm is k-means. In the first step, the initial centroids are needed for k-means where they are often randomly determined. To get them, this paper would utilize some preprocessing, namely Silhouette Density Canopy (SDC), Density Canopy (DC), Silhouette (S), Elbow (E), and Bayesian Information Criterion (BIC). Then, the clustering results by using those preprocessing were compared to obtain the optimal clustering. The comparison showed that the optimal clustering had been given by k-means using Elbow where obtain four clusters and 0.6772 Silhouette index value in dataset used. The other results showed that k-means using Elbow was better than k-means without preprocessing where the odds were 0.75. Interpretation of the optimal clustering is that there are three lecturers with the lower teaching values, namely N16, N25, and N84.
PENENTUAN JUMLAH CLUSTER IDEAL SMK DI JAWA TENGAH DENGAN METODE X-MEANS CLUSTERING DAN K-MEANS CLUSTERING Rifki Adhitama; Auliya Burhanuddin; Ridho Ananda
JIKO (Jurnal Informatika dan Komputer) Vol 3, No 1 (2020)
Publisher : Journal Of Informatics and Computer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v3i1.1635

Abstract

SMK merupakan salah satu intrumen penting dalam pengembangan Sumber Daya Manusia (SDM) di Indonesia pada umumnya dan di Jawa Tengah pada khususnya. Belum adanya pengelompokan SMK berdasarkan data pokok kemendikbud di jawa tengah merupakan sebuah peluagn untuk mengembangkan arah revitalisasi SMK menjadi lebih baik dan jelas. X-means merupakan salah satu metode clustering yang dikembangkan dari metode clustering yang cukup popular, yaitu K-means. Penelitian ini menggunakan data pokok kemendikbud untuk menghitung pembagian cluster terbaik dengan menggunakan metode X-means dengan membandingkan nilai DBI X-means dengan nilai DBI K-means pada variasi ukuran cluster mulai dari empat, enam, delapan dan sepuluh cluster. Hasil penelitian ini menunjukkan bahwa secara konsisten nilai DBI terbaik ada pada ukuran cluster empat, baik menggunakan X-means ampun K-means dengan nilai DBI X-means sebesar 0,933 dan nilai DBI K-means sebesar 0,914, sedangkan nilai DBI paling besar juga konsisten pada ukuran cluster 10, sebesar 1,439 pada X-means dan 1,322 pada K-means. Berdasrkan hasil tersebut maka SMK di Jawa Tengah dapat dibagi ke dalam 4 kelompok yaitu kurang, cukup, baik, dan unggul.
Analisis Mutu Pendidikan Sekolah Menengah Atas Program Ilmu Alam di Jawa Tengah dengan Algoritme K-Means Terorganisir Ridho Ananda
Journal of INISTA Vol 2 No 1 (2019): November 2019
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v2i1.97

Abstract

Banyaknya jumlah sekolah menengah atas (SMA) di Jawa Tengah dengan mutu pendidikan yang berbeda-beda menjadi kendala bagi stakeholder dalam mengambil kebijakan. Untuk mengatasinya, dibutuhkan metode dalam menganalisis data sekolahan yang berkaitan dengan mutu pendidikan. Salah satu metode yang dapat digunakan adalah penggerombolan. Pada penelitian ini diterapkan metode penggerombolan dengan algoritme K-means serta kombinasi K-means dan Hirarki pada nilai ujian akhir nasional (UAN) program ilmu alam. Nilai UAN merupakan salah satu komponen penilaian mutu pendidikan. Penentuan banyak gerombol optimal digunakan Bayesian Information Criterion (BIC) dan diperoleh 5 gerombol optimal dengan BIC 221.45. Hasil penggerombolan terbaik berdasarkan nilai Silhouette diperoleh algoritme complete K-means dengan nilai 0.4537, sehingga hasil tersebut digunakan untuk menganalisis mutu pendidikan di Jawa Tengah. Berdasarkan hasil penggerombolan, diperoleh kesimpulan bahwa sekolah yang unggul banyak terdapat di kota Semarang dengan proporsi 12.76% dari seluruh sekolah unggul pada 35 wilayah di Jawa Tengah. Sedangkan sekolah terbanyak pada peringkat terendah di Boyolali dengan proporsi 9.03% dari seluruh sekolah peringkat terakhir pada 35 wilayah di Jawa Tengah. Lima wilayah yang perbedaan mutunya tidak merata ialah Banjarnegara, Demak, Kab. Pekalongan, Batang, dan Purwodadi. Sedangkan lima wilayah yang perbedaan mutunya paling merata adalah Wonosobo, Tegal, Semarang, dan Magelang.
Co-Authors 12.5202.0161 Daniel Yeri Kristiyanto Achmad Zaki Yamani Agi Prasetiadi Al Atsary, Arry Darmawan Aliyah, Khikmatul Amalia Beladinna Arifa Anggraeni, Nadia Putri Anita Chindyana Ardian, Miko Arry Darmawan Asminah Aswan Munang Atika Ratna Dewi Auliya Burhanuddin Awanda, Yulia Bangkit Wiguna Devindra, Sheisha Nabila Dhau, Shadana Faturohman, Muhammad Iqbal Fauzan Romadlon Fipiariny Gushelmi Gushelmi Gushelmi Hadi Kurniawan Hadi Kurniawan, Hadi Isnaini Nurisusilawati Jaya, Satria Luthfi Ikhsan Al Ghani Maidawati Maidawati Maifuza Binti Mohd Amin Miftahul Huda Muhammad Afif Muhammad Ardiansyah Muhammad Zidny Naf'an Nainggolan, Racha Defitri Nurlaili Nursapia Harahap Rani Septiani Sukandar Rifki Adhitama, Rifki Rimbawati Saufik Luthfianto Saufik Luthianto Setiadi, Heri Siregar, Salsa Fadhilah Utti Marina Rifanti Wahyu Adi Prabowo Yoga, Wira Yudha Saintika Zulkarnain, Nur Afrina Huda