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All Journal Jurnal INFOTEL Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Gantang Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jiko (Jurnal Informatika dan komputer) MOTIVECTION : Journal of Mechanical, Electrical and Industrial Engineering INISTA

Ridho Ananda

Institut Teknologi Telkom Purwokerto

Author-ID : 2489348

Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Articles

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

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.

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

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)

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)

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.

Classification Based on Configuration Objects by Using Procrustes Analysis Ridho Ananda; Agi Prasetiadi
JURNAL INFOTEL Vol 13 No 2 (2021): May 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Classification is one of the data mining topics that will predict an object to go into a certain group. The prediction process can be performed by using similarity measures, classification trees, or regression. On the other hand, Procrustes refers to a technique of matching two configurations that have been implemented for outlier detection. Based on the result, Procrustes has a potential to tackle the misclassification problem when the outliers are assumed as the misclassified object. Therefore, the Procrustes classification algorithm (PrCA) and Procrustes nearest neighbor classification algorithm (PNNCA) were proposed in this paper. The results of those algorithms had been compared to the classical classification algorithms, namely k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), AdaBoost (AB), Random Forest (RF), Logistic Regression (LR), and Ridge Regression (RR). The data used were iris, cancer, liver, seeds, and wine dataset. The minimum and maximum accuracy values obtained by the PrCA algorithm were 0.610 and 0.925, while the PNNCA were 0.610 and 0.963. PrCA was generally better than k-NN, SVM, and AB. Meanwhile, PNNCA was generally better than k-NN, SVM, AB, and RF. Based on the results, PrCA and PNNCA certainly deserve to be proposed as a new approach in the classification process.

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

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 Kemampuan Komunikasi Matematis Mahasiswa pada Aplikasi Graf Menggunakan Pendekatan MEAs Nurlaili; Utti Marina Rifanti; Ridho Ananda
Jurnal Gantang Vol 5 No 2 (2020): Jurnal Gantang Volume 5 Nomor 2 September 2020
Publisher : Universitas Maritim Raja Ali Haji

Kemampuan berpikir matematis dalam pembelajaran rekayasa atau keteknikan merupakan kemampuan mendasar dalam pencapaian pembelajaran lulusan. Peningkatan kemampuan berpikir matematis khususnya kemampuan komunikasi juga harus diupayakan dalam pembelajaran Matematika Diskrit bagi mahasiswa Teknik Telekomunikasi khususnya aplikasi graf. Kajian yang dilakukan dalam penelitian ini bertujuan untuk menganalisis kemampuan komunikasi mahasiswa menggunakan pendekatan MEAs dalam penyelesaian masalah pada aplikasi graf. Penelitian yang dilakukan merupakan penelitian kualitatif dengan metode penyajian secara deskriptif dan pendekatan studi kasus. Subjek penelitian berjumlah 29 mahasiswa yang berasal dari satu kelas pada Mata Kuliah Matematika Diskrit Prodi Teknik Telekomunikasi Tahun Ajaran 2019/2020. Hasil penelitian menujukkan bahwa kemampuan komunikasi mahasiswa sebesar 31,03% kategori tinggi, 27,59% kategori sedang, 41,38% kategori rendah. Mahasiswa kemampuan tinggi sudah mampu mengkomunikasikan jawaban secara matematis dengan tepat. Pada mahasiswa yang memiliki kemampuan komunikasi sedang sudah mampu untuk mengkomunikasikan jawaban secara matematis namun masih ada kekurangan. Sedangkan mahasiswa yang memiliki kemampuan rendah kurang mampu mengkomunikasikan jawaban secara matematis dengan tepat. Perancangan materi pembelajaran dengan focus pada peningkatan kemampuan komunikasi khususnya komunikasi matematis mahasiswa masih dibutuhkan dalam pembelajaran aplikasi graf.

Implementation of Quality Control to Overcome Defective Tile Production With the Application of Statistical Process Control (SPC) Methods Luthfi Ikhsan Al Ghani; Isnaini Nurisusilawati; Ridho Ananda
MOTIVECTION : Journal of Mechanical, Electrical and Industrial Engineering Vol 4 No 3 (2022): Motivection : Journal of Mechanical, Electrical and Industrial Engineering
Publisher : Indonesian Mechanical Electrical and Industrial Research Society (IMEIRS)

One of the main factors to improve product quality is to carry out quality control. The purpose of this research is to reduce or suppress the number of defective tile products produced by controlling quality. The analytical method used in this research is Statistical Process Control (SPC) by calculating the percentage of damage to the product, CL, UCL, and LCL. The results of this study found that quality control in Super Soka Masinal Tile UKM was not controlled, this can be seen based on the calculation that there are 4 points outside the control limit so that the number of rejects exceeds the maximum allowable limit. And based on the Pareto diagram, it is known that the crack defect is the highest defect with a percentage of 44%. Therefore, it is necessary to propose improvements to crack defects using a fishbone diagram. Salah satu faktor utama untuk meningkatkan kualitas produk adalah dengan melakukan pengendalian kualitas. Tujuan pada penelitian ini adalah untuk mengurangi ataupun menekan jumlah pada produk cacat genteng yang dihasilkan dengan cara melakukan pengendaian kualitas. Metode analisis yang digunakan dalam penelitian ini adalah Statistical Process Control (SPC) dengan menghitung persentase kerusakan pada produk, CL, UCL, dan LCL. Hasil penelitian ini ditemukan bahwa pengendalian kualitas di UKM Genteng Super Soka Masinal tidak terkendali, ini dapat dilihat berdasarkan perhitungan terdapat 4 titik berada di luar batas kendali sehingga jumlah reject yang ada melebihi batas maksimal yang diperbolehkan. Dan berdasarkan diagram pareto, diketahui bahwa cacat retak merupakan cacat yang tertinggi dengan persentase 44%. Oleh karena itu perlu dilakukannya usulan perbaikan pada cacat retak dengan menggunakan fishbone diagram.

Co-Authors Achmad Zaki Yamani Agi Prasetiadi Amalia Beladinna Arifa Auliya Burhanuddin Luthfi Ikhsan Al Ghani Muhammad Zidny Naf’an Nurisusilawati, Isnaini Nurlaili Rifki Adhitama Utti Marina Rifanti

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