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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 Teknik Informatika (JUTIF) 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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Garbage Classification Using Ensemble DenseNet169 Ulfah Nur Oktaviana; Yufis Azhar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Garbage is a big problem for the sustainability of the environment, economy, and society, where the demand for waste increases along with the growth of society and its needs. Where in 2019 Indonesia was able to produce 66-67 million tons of waste, which is an increase from the previous year of 2 to 3 million tons of waste. Waste management efforts have been carried out by the government, including by making waste sorting regulations. This sorting is known as 3R (reduce, reuse, recycle), but most people do not sort their waste properly. In this study, a model was developed that can sort out 6 types of waste including: cardboard, glass, metal, paper, plastic, trash. The model was built using the transfer learning method with a pretrained model DenseNet169. Where the optimal results are shown for the classes that have been oversampling previously with an accuracy of 91%, an increase of 1% compared to the model that has an unbalanced data distribution. The next model optimization is done by applying the ensemble method to the four models that have been oversampled on the training dataset with the same architecture. This method shows an increase of 3% to 5% while the final accuracy on the test of dataset is 96%.

Pneumonia Image Classification Using CNN with Max Pooling and Average Pooling Annisa Fitria Nurjannah; Andi Shafira Dyah Kurniasari; Zamah Sari; Yufis Azhar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Pneumonia is still a frequent cause of death in hundreds of thousands of children in most developing countries and is generally detected clinically through chest radiographs. This method is still difficult to detect the disease and requires a long time to produce a diagnosis. To simplify and shorten the detection process, we need a faster method and more precise diagnosis of pneumonia. This study aims to classify chest x-ray images using the CNN method to diagnose pneumonia. The proposed CNN model will be tested using max & average pooling. The proposed model is developed in previous studies by adding batch normalization, dropout layer, and the number of epochs used. The dataset used will be optimized with oversampling & data augmentation techniques to maximize model performance. The dataset used in this study is "Chest X-Ray Images (Pneumonia)," with 5,856 data divided into two classes, namely Normal and Pneumonia. The proposed model gets 98% results using average pooling, where the results increase by 9-13% better than the previous study. This is because the overall pixel value of the image is highly considered to classify normal lungs and pneumonia.

Prediksi Pendapatan Kargo Menggunakan Arsitektur Long Short Term Memory Bagas Aji Aprian; Yufis Azhar; Vinna Rahmayanti Setya Nastiti
Jurnal Komputer Terapan Vol. 6 No. 2 (2020): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Indonesian air cargo transportation is currently experiencing quite significant development. One of the cargo services in Indonesia is Garuda Indonesia Cargo and has several branch offices. The existence of a revenue forecast model is expected to provide insight into a branch office. This research proposes an income prediction using the Deep Learning algorithm, Long Short Term Memory (LSTM). LSTM is used because the data to be processed is time series data. The results of testing accuracy are measured using Root Mean Squared Error (RMSE). The data in this study are income from one branch office, the Cargo Service Center (CSC) Tangerang City. Data contains collections of goods delivery transactions every day. The data goes through 4 preprocessing processes, namely subtotal, outlier detection, difference, and scaling. The results of this study show the best prediction results, namely the composition of the 90% train data and 10% test data with RMSE values of train data 641375.70 and test data 594197.70.

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%.

METODE LEXICON-LEARNING BASED UNTUK IDENTIFIKASI TWEET OPINI BERBAHASA INDONESIA Yufis Azhar
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 6 No. 3 (2017)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v6i3.11739

Media sosial telah lama digunakan masyarakat untuk menyampaikan opini maupun fakta terhadap suatu kejadian, khusunya twitter. Banyak metode yang diusulkan untuk mengekstrak tweet yang berisi opini. Diantaranya mengunakan pendekatan identifikasi kata kunci dalam suatu tweet yang lebih dikenal dengan istilah lexicon based. Meskipun metode ini memiliki nilai presisi yang cukup tinggi dalam mengidentifikasi suatu tweet opini, akan tetapi nilai recall yang dihasilkan cukup rendah. Hal ini karena keterbatasan lexicon yang digunkan sebagai identifier. Dalam penelitian ini, diusulkan kombinasi metode lexicon based dan machine learning dalam mengoptimalkan hasil identifikasi tweet opini. Hasil pengujian menunjukkan peningkatan nilai recall yang cukup signifikan jika dibandingkan dengan metode lexicon based dengan tetap menjaga nilai precision.

Detection of Covid-19 on X-Ray Image of Human Chest Using CNN and Transfer Learning Jalu Nusantoro; Faldo Fajri Afrinanto; Wana Salam Labibah; Zamah Sari; Yufis Azhar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

At the end of 2019, a new disease called Coronavirus Disease (COVID-19) originated in Wuhan, China. This disease is caused by respiratory tract infections, ranging from the common cold to serious diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). In Indonesia, there are tests to detect COVID-19, such as PCR and Rapid Test. This detector takes a long time and is less accurate in producing a diagnosis. This study aims to classify chest X-ray images using CNN and Transfer Learning methods to diagnose COVID-19. The proposed model has 4 scenarios: CNN Handcraft Model, Transfer Learning (VGG 16, VGG 19, and ResNet 50). This model is accompanied by data augmentation and data balancing techniques using undersampling techniques. The dataset used in this study is the “Covid-19 (COVID-19 and Normal) Radiographic Database” with 13,808 data divided into two classes, namely COVID-19 and Normal. Each model built will produce values for accuracy, precision, recall, and confusion matrix. The results of CNN Scenario 1 accuracy is 95%, in Scenario 2 VGG 16 the accuracy is 93%, Scenario 3 VGG 19 is 90% and Scenario 4 ResNet 50 is 80%.

Pengelompokan Kata Berdasarkan Kemiripan Ucapan Pada Kamus Menggunakan Algoritma Metaphone Pada Sistem Operasi Android Aditya Dwi Maryanto; Yuda Munarko; Yufis Azhar
Jurnal Repositor Vol 1 No 1 (2019): November 2019
Publisher : Universitas Muhammadiyah Malang

Indonesia merupakan negara kepulauan yang terdiri dari berbagai macam suku dan budaya serta bahasa yang berbeda-beda. Salah satunya adalah bahasa Sumbawa yang digunakan oleh masyarakat bagian barat pulau Sumbawa. Bahasa Sumbawa terdapat 4 dialek yaitu Samawa, Taliwang, Jereweh, dan Tongo. Dalam bahasa Sumbawa juga terdapat kata-kata yang bersifat homofon, homonim, dan homograf. Pengelompokkan kata berdasarkan kemiripan ucapan pada kamus Sumbawa dengan algoritma Metaphone pada operating sistem android menjadi pembahasan pada penelitian ini. Algoritma Metaphone dapat diterapkan pada berbagai bahasa dengan aturan yang telah dimodifikasi sesuai dengan karakterisitik bahasa yang diinginkan. Berdasarkan hasil pengujian recall dan precision pada aturan baru algoritma Metaphone dapat disimpulkan bahwa pengelompokkan kata berdasarkan kemiripan ucapan dalam bahasa Sumbawa dapat dikatakan efektif. Indeks persentase rata-rata pengujian recall 98,97% dan precision 78,52%.

Co-Authors A.A. Ketut Agung Cahyawan W Achmad Fauzi Saksenata 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 asqalani, Sheila Fitria 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 Aripa, Laofin Aris Muhandisin arrafiq, ubay hakim 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 Cokro Mandiri, Mochammad Hazmi Denny Risky Delis Putra Dewi Agfiannisa Diana Purwitasari Doni Yulianto Dwi Anggraini Puspita Rahayu Dwi Kurnia Puspitaningrum DWI RAHMAWATI Dyah Anitia Dyah Ayu Irianti Eko Budi Cahyono 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 Hanung Adi Nugroho Haqim, Gilang Nuril Hardianto Wibowo Haris Diyaul Fata Harmanto, Dani Hasanuddin, Muhammad Yusril 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 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 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 Rizki Muhammad Rizky Iman Permana Muhammad Shalahuddin Zulva 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 Pratama, Dhimas Rama Anthony Navy 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 S, Vinna Rahmayanti Saputri, Indah Sari Wahyunita Sari, Veronica Retno Sari, Zamah Satrio Hadi Wijoyo Septiyan Andika Isanta Setiono, Fauzan Adrivano 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 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 Yusuf, Achmad Zamah Sari Zulva, Muhammad Shalahuddin

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