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All Journal Jurnal Dedikasi Jurnal Ilmu Komputer Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Simantec Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Teknologi Informasi dan Ilmu Komputer SMATIKA Proceeding of the Electrical Engineering Computer Science and Informatics Fountain of Informatics Journal Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika Jurnal Pilar Nusa Mandiri Network Engineering Research Operation [NERO] Jurnal Komputer Terapan Syntax Literate: Jurnal Ilmiah Indonesia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control SINTECH (Science and Information Technology) Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURTEKSI EDUMATIC: Jurnal Pendidikan Informatika Jurnal Informatika Kaputama (JIK) JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Repositor Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Perempuan & Anak Jurnal Dinamika Informatika (JDI) Makara Journal of Technology Jurnal Sistem Informasi Jurnal Informatika: Jurnal Pengembangan IT

Yufis Azhar

Universitas Muhammadiyah Malang

Author-ID : 237577

Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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The Analysis of Proximity Between Subjects Based on Primary Contents Using Cosine Similarity on Lective Al-rizki, Muhammad Andi; Wicaksono, Galih Wasis; Azhar, Yufis
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 2, No 4, November-2017
Publisher : Universitas Muhammadiyah Malang

In education world, recognizing the relationship between one subject and another is imperative. By recognizing the relationship between courses, performing sustainability mapping between subjects can be easily performed.Â Moreover, detecting and reducing any duplicated contents in several subjects will be also possible to execute. Of course, these conveniences will benefit lecturers, students and departments. It will ease the analysis and discussion processes between lecturers related to subjects in the same domain. In addition, students will conveniently choose a group of subjects they are interested in. Furthermore, departments can easily create a specialization group based on the similarity of the subjects and combine the courses possessing high similarity. In this research, given a good database, the relationship between subjects was calculated based on the proximity of the primary contents of the subjects. The feature used was term feature, in which value was determined by calculating TF-IDF (Term Frequency Inverse Document Frequency) from each term. In recognizing the value of proximity between subjects, cosine similarity method was implemented. Finally, testing was done utilizing precision, recall and accuracy method. The research results show that the precision and accuracy values are 90,91% and the recall value is 100%.

Peringkasan Tweet Berdasarkan Trending Topic Twitter Dengan Pembobotan TF-IDF dan Single Linkage Angglomerative Hierarchical Clustering Annisa, Annisa; Munarko, Yuda; Azhar, Yufis
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 1, No 1, May-2016
Publisher : Universitas Muhammadiyah Malang

Fitur yang paling sering digunakan pada Twitter ialah Trending Topic. Trending Topic merupakan fitur yang menampilkan beberapa hashtag berisi topik yang sedang trend saat ini. Jika pengguna ingin mengetahui informasi mengenai suatu trending topic, pengguna bisa mengklik salah satu hashtag dan barulah muncul beberapa tweet terkait dengan hashtag tersebut. Agar menghemat waktu pengguna Twitter dalam membaca suatu trending topic tanpa perlu membaca beberapa tweet terlebih dahulu, maka dilakukanlah analisa dengan tujuan membuat text summarization untuk trending topic pada Twitter menggunakan algoritma TF-IDF dan Single Linkage Agglomerative Hierarchical Clustering. Penelitian ini menggunakan 100 trending topic untuk data tes pada sistem dan setiap trending topic terdiri atas 50 tweet berbahasa indonesia, sedangkan untuk pengujian digunakan 30 data trending topic diambil secara acak (data mewakili trending topic dengan sub tema minimal 2 dan maksimal 9 dari 100 data tes pada sistem). Dari 30 data pengujian, 1 data menghasilkan semua ringkasan sama persis dengan ahli,Â dan 29 data menghasilkan 1-4Â ringkasan sama persis dengan ahli (terdiri atas 2-9 ringkasan untuk setiap trending topic).

Feature Selection on Pregnancy Risk Classification Using C5.0 Method Azhar, Yufis; Afdian, Riz
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 3, No 4, November 2018
Publisher : Universitas Muhammadiyah Malang

The maternal mortality rate in Indonesia is still relatively high. This is caused by several factors, including the ignorance of pregnant women about the risk status of pregnancy. Several methods are proposed for early detection of the risk of a mothers pregnancy. However, no one has highlighted what features are most influential in the process of classifying the risk of pregnancy. In this research, we use data of pregnant women in one of the health centers in Malang, Indonesia, as a dataset. The dataset has 107 features, therefore, feature selection is needed for the classification process. We propose to use the C5.0 method to select important features while classifying dataset into low, high, and very high risk of pregnancy. C5.0 was chosen because this method has a better pruning algorithm and requires relatively smaller memory compared to C4.5. Another classification method (SVM, Naive Bayes, and Nearest Neighbor) is then used to compare the accuracy values between datasets that use all features with datasets that only use the selected features. The test results show that feature selection can increase accuracy by up to 5%.

Image Retrieval Based on Texton Frequency-Inverse Image Frequency Azhar, Yufis; Minarno, Agus Eko; Munarko, Yuda; Ibrahim, Zaidah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 2, May 2020
Publisher : Universitas Muhammadiyah Malang

In image retrieval, the user hopes to find the desired image by entering another image as a query. In this paper, the approach used to find similarities between images is feature weighting, where between one feature with another feature has a different weight. Likewise, the same features in different images may have different weights. This approach is similar to the term weighting model that usually implemented in document retrieval, where the system will search for keywords from each document and then give different weights to each keyword. In this research, the method of weighting the TF-IIF (Texton Frequency-Inverse Image Frequency) method proposed, this method will extract critical features in an image based on the frequency of the appearance of texton in an image, and the appearance of the texton in another image. That is, the more often a texton appears in an image, and the less texton appears in another image, the higher the weight. The results obtained indicate that the proposed method can increase the value of precision by 7% compared to the previous method.

Peringkasan Tweet Berdasarkan Trending Topic Twitter Dengan Pembobotan TF-IDF dan Single Linkage Angglomerative Hierarchical Clustering Annisa Annisa; Yuda Munarko; Yufis Azhar
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 1, No 1, May-2016
Publisher : Universitas Muhammadiyah Malang

Fitur yang paling sering digunakan pada Twitter ialah Trending Topic. Trending Topic merupakan fitur yang menampilkan beberapa hashtag berisi topik yang sedang trend saat ini. Jika pengguna ingin mengetahui informasi mengenai suatu trending topic, pengguna bisa mengklik salah satu hashtag dan barulah muncul beberapa tweet terkait dengan hashtag tersebut. Agar menghemat waktu pengguna Twitter dalam membaca suatu trending topic tanpa perlu membaca beberapa tweet terlebih dahulu, maka dilakukanlah analisa dengan tujuan membuat text summarization untuk trending topic pada Twitter menggunakan algoritma TF-IDF dan Single Linkage Agglomerative Hierarchical Clustering. Penelitian ini menggunakan 100 trending topic untuk data tes pada sistem dan setiap trending topic terdiri atas 50 tweet berbahasa indonesia, sedangkan untuk pengujian digunakan 30 data trending topic diambil secara acak (data mewakili trending topic dengan sub tema minimal 2 dan maksimal 9 dari 100 data tes pada sistem). Dari 30 data pengujian, 1 data menghasilkan semua ringkasan sama persis dengan ahli, dan 29 data menghasilkan 1-4 ringkasan sama persis dengan ahli (terdiri atas 2-9 ringkasan untuk setiap trending topic).

POS Tagger Tweet Bahasa Indonesia Yuda Munarko; yufis azhar; Maulina Balqis; Susi Ekawati
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 2, No 1, February-2017
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v2i1.169

Pada penelitian ini dilakukan investigasi POS Tagger dengan pendekatan Cyclic Dependency Network untuk data tweet dalam Bahasa Indonesia. Untuk koleksi tweet, digunakan tiga koleksi data, yakni tweet dengan gaya bahasa formal, informal dan gabungan. Sumber koleksi tweet formal adalah tweet dari akun berita, sedangkan koleksi tweet informal didapatkan dari akun umum. Adapun jenis tag yang digunakan berjumlah 41, dimana 35 adalah standar tag Bahasa Indonesia dan 6 adalah tambahan tag untuk twitter. Hasilnya adalah untuk koleksi data formal ketepatan deteksi mencapai 95,42%. Sedangkan untuk koleksi data informal dan gabungan ketepatannya mencapai 92,42% dan 90,69% secara berurutan. Kami juga mendapatkan hasil bahwa untuk tag yang sering muncul cenderung untuk memiliki nilai ketepatan yang tinggi juga, sedangkan tag yang kemunculannya lebih sedikit menyebabkan penurunan rata-rata ketepat secara keseluruhan.

The Analysis of Proximity Between Subjects Based on Primary Contents Using Cosine Similarity on Lective Muhammad Andi Al-rizki; Galih Wasis Wicaksono; Yufis Azhar
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 2, No 4, November-2017
Publisher : Universitas Muhammadiyah Malang

In education world, recognizing the relationship between one subject and another is imperative. By recognizing the relationship between courses, performing sustainability mapping between subjects can be easily performed. Moreover, detecting and reducing any duplicated contents in several subjects will be also possible to execute. Of course, these conveniences will benefit lecturers, students and departments. It will ease the analysis and discussion processes between lecturers related to subjects in the same domain. In addition, students will conveniently choose a group of subjects they are interested in. Furthermore, departments can easily create a specialization group based on the similarity of the subjects and combine the courses possessing high similarity. In this research, given a good database, the relationship between subjects was calculated based on the proximity of the primary contents of the subjects. The feature used was term feature, in which value was determined by calculating TF-IDF (Term Frequency Inverse Document Frequency) from each term. In recognizing the value of proximity between subjects, cosine similarity method was implemented. Finally, testing was done utilizing precision, recall and accuracy method. The research results show that the precision and accuracy values are 90,91% and the recall value is 100%.

Feature Selection on Pregnancy Risk Classification Using C5.0 Method Yufis Azhar; Riz Afdian
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 3, No 4, November 2018
Publisher : Universitas Muhammadiyah Malang

The maternal mortality rate in Indonesia is still relatively high. This is caused by several factors, including the ignorance of pregnant women about the risk status of pregnancy. Several methods are proposed for early detection of the risk of a mother's pregnancy. However, no one has highlighted what features are most influential in the process of classifying the risk of pregnancy. In this research, we use data of pregnant women in one of the health centers in Malang, Indonesia, as a dataset. The dataset has 107 features, therefore, feature selection is needed for the classification process. We propose to use the C5.0 method to select important features while classifying dataset into low, high, and very high risk of pregnancy. C5.0 was chosen because this method has a better pruning algorithm and requires relatively smaller memory compared to C4.5. Another classification method (SVM, Naive Bayes, and Nearest Neighbor) is then used to compare the accuracy values between datasets that use all features with datasets that only use the selected features. The test results show that feature selection can increase accuracy by up to 5%.

Image Captioning using Hybrid of VGG16 and Bidirectional LSTM Model Yufis Azhar; M. Randy Anugerah; Muhammad Al Reza Fahlopy; Alfin Yusriansyah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 4, November 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i4.1568

Image captioning is one of the biggest challenges in the fields of computer vision and natural language processing. Many other studies have raised the topic of image captioning. However, the evaluation results from other studies are still low. Thus, this study focuses on improving the evaluation results from previous studies. In this study, we used the Flickr8k dataset and the VGG16 Convolutional Neural Networks (CNN) model as an encoder to generate feature extraction from images. Recurrent Neural Network (RNN) uses the Bidirectional Long-Short Term Memory (BiLSTM) method as a decoder. The results of the image feature extraction process in the form of feature vectors are then forwarded to Bidirectional LSTM to produce descriptions that match the input image or visual content. The captions provide information on the object’s name, location, color, size, features of an object, and surroundings. A greedy Search algorithm with Argmax function and Beam-Search algorithm are used to calculate Bilingual Evaluation Understudy (BLEU) scores. The results of the evaluation of the best BLEU scores obtained from this study are the VGG16 model with Bidirectional LSTM using Beam Search with parameter K = 3 and the BLEU-1 score is 0.60593, so this score is superior to previous studies.

The Implementation of Pretrained VGG16 Model for Rice Leaf Disease Classification using Image Segmentation Suseno, Jody Ririt Krido; Azhar, Yufis; Minarno, Agus Eko
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 1, February 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i1.1592

Rice is an agricultural sector that produces rice which is one of the staple foods for the majority of the population in Indonesia. In the cultivation of rice plants there are also factors that affect rice production and are not realized by farmers causing that they are late in handling and diagnosing symptoms and making rice production decline. Therefore, it is necessary to have an early diagnosis of rice plants to identify them correctly, quickly and accurately. Machine learning is one of the classification techniques to detect various plant diseases such as rice plants. There are several studies on machine learning using the Convolutional Neural Network with the VGG16 model to classify rice leaf diseases and using Image Segmentation techniques on rice leaf datasets for make the image becomes a form that is not too complicated to analyze. The data used in this research is Rice Leaf Disease which consists of 3 classes including Bacterial leaf blight, Brown spot, and Leaf smut. Then segmentation is carried out using two techniques, namely threshold and k means. Then data augmentation for make dataset used has a large and varied number and training using VGG16 model with hyperparameter tuning and obtained 91.66% accuracy results for scenarios with the k-means dataset.

Co-Authors A.A. Ketut Agung Cahyawan W Achmad Fauzi Saksenata Achmad Yusuf Adhigana Priyatama Aditya Dwi Maryanto Adnan Burhan Hidayat Kiat Afdian, Riz Agus Eko Minarno Agus Zainal Arifin Ahmad Annas Al Hakim Ahmad Darman Huri Ahmad Hanif Nurfauzi Ahmadu Kajukaro Akbi, Denar Regata Akmal Muhammad Naim Al-rizki, Muhammad Andi Alfin Yusriansyah Ali Sofyan Kholimi Amelia, Putri Juli Ananda Ayu Dianti Andhika Ade Verdiyanto Andhika Pranadipa Andi Shafira Dyah Kurniasari Andreawana Andreawana Andriani Eka Pramudita Annisa Annisa Annisa Fitria Nurjannah Aria Maulana Aris Muhandisin Arya, Tri Fidrian Audi Bayu Yuliawan Aulia Ligar Salma Hanani Bagas Aji Aprian Basuki, Setio Bayu Yuliawan, Audi Bintang, Rahina Chandranegara, Didih Rizki Chita Nauly Harahap Christian Sri Kusuma Aditya Christian Sri kusuma Aditya, Christian Sri kusuma Denny Risky Delis Putra Dewi Agfiannisa Diana Purwitasari Diana Purwitasari Doni Yulianto Dwi Anggraini Puspita Rahayu Dwi Kurnia Puspitaningrum DWI RAHMAWATI Dyah Anitia Dyah Ayu Irianti Eko Budi Cahyono Elfrida Ratnawati Elsyah Ayuningrum Elza Norazizah Evi Febrion Rahayuningtyas Fahrur Rozi Faizun Nuril Hikmah Faldo Fajri Afrinanto Fatimah Defina Setiti Alhamdani Fenny Linsisca Putri Feny Novia Rahayu Feranandah Firdausi Ferin Reviantika Ferin Reviantika Fikri, Ulul Fiqri Azmi Fachir Firdausi, Feranandah Firdausita, Nuris Sabila Firdausy, Aidia Khoiriyah Firdhansyah Abubekar Fitri Bimantoro Galang Aji Mahesa Galang Aji Mahesa Gita Indah Marthasari Haqim, Gilang Nuril Hardianto Wibowo Haris Diyaul Fata Harmanto, Dani Hermansyah Adi Saputra Hiu Adam Abdullah Hussin Agung Wijaya Ibrahim, Zaidah Ilham Rahmana Syihad Imam Halimi Irfan, Muhammad Ivan Dwi Nugraha Jahtra Hidayatullah Jalu Nusantoro Khoirir Rosikin Kiki Ratna Sari Laofin Aripa Lina Dwi Yulianti Linggar Bagas Saputro Lusianti, Aaliyah M Syawaluddin Putra Jaya M. Randy Anugerah Mahar Faiqurahman Maskur Maskur Maskur Maskur Masluha, Ida Maulina Balqis Meilina Agustina Mentari Mas'ama Safitri Moch Shandy Tsalasa Putra Moch. Chamdani Mustaqim Mochammad Hazmi Cokro Mandiri Mochammad Hazmi Cokro Mandiri Moh. Badris Sholeh Rahmatullah Muhammad Aji Purnama Wibowo Muhammad Al Reza Fahlopy Muhammad Andi Al-Rizki Muhammad Athaillah Muhammad Bima Al Fayyadl Muhammad Fadliansyah Muhammad Hussein Muhammad Misbahul Azis Muhammad Nuchfi Fadlurrahman Muhammad Riadi Muhammad Rifal Alfarizy Muhammad Rivaldi Asyhari Muhammad Rizal Muhammad Rizki Muhammad Rizky Iman Permana Muhammad Shalahuddin Zulva Muhammad Yusril Hasanuddin Mujaddid Izzul Fikri Nabillah Annisa Rahmayanti Nina Mauliana Noor Fajriah Novandha Yudyanto Noviani Sintia Duwi Trisna Nur Hayatin Nur Putri Hidayah Nuryasin, Ilyas Oktavia Dwi Megawati Otto Endarto Prakoso, Rahmat Putri, Ira Ekanda Rahma Ningsih Rangga Kurnia Putra Wiratama Ratna Sari Rifky Ahmad Saputra Riksa Adenia Riska Septiana Putri Rista Azizah Arilya Riz Afdian Rizal Arya Suseno Rizal Rakhman Mustafa Rozi, Fahrur S, Vinna Rahmayanti Saputri, Indah Sari Wahyunita Sari, Veronica Retno Sari, Zamah Satrio Hadi Wijoyo Satrio Hadi Wijoyo Septiyan Andika Isanta Setiono, Fauzan Adrivano Sheila Fitria Al asqalani Shintya Larasabi , Auliya Tara Silcillya Ayu Astiti Siti Maghfiroh Sucia, Dara Suryani Rachmawati Suseno, Jody Ririt Krido Susi Ekawati Syaifuddin Syaifuddin Syaifudin Zuhri Taufik Nurahman Tri Fidrian Arya Trifebi Shina Sabrila Trifebi Shina Sabrila ubay hakim arrafiq Ujilast, Novia Adelia Ulfah Nur Oktaviana Veronica Retno Sari Vinna Utami Putri Wahyu Priyo Wicaksono Wana Salam Labibah Wicaksono, Galih Wasis Widya Rizka Ulul Fadilah Wildan Suharso Wildan Suharso Wildan Suharso Yesicha Amilia Putri Yuda Munarko Yudhono Witanto Yurizal Rizqon Rifani Zaidah Ibrahim Zamah Sari

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Journal : Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control