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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SISFOTENIKA Journal of Information Technology and Computer Science (JOINTECS) Emerging Science Journal Insect (Informatics and Security): Jurnal Teknik Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer CYBERNETICS Literasi Hukum Buletin Ilmiah Sarjana Teknik Elektro International Journal of Robotics and Control Systems Jurnal Locus Penelitian dan Pengabdian Jurnal Pengabdian Masyarakat Jurnal Ilmu Komputer dan Teknologi (IKOMTI) SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia Journal of Advanced Health Informatics Research SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Multidisiplin Pengabdian Masyarakat (JMPM)

Purwono, Purwono

Universitas Harapan Bangsa

Author-ID : 2289622

Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Implementation of Dijkstra Algorithm with React Native to Determine Covid-19 Distribution Rosyid Ridlo Al Hakim; Purwono Purwono; Yanuar Zulardiansyah Arief; Agung Pangestu; Muhammad Haikal Satria; Eko Ariyanto
Sistemasi: Jurnal Sistem Informasi Vol 11, No 1 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Since Covid-19 was declared a global pandemic because it has spread throughout the world, every effort has been made to help prevent and tackle the transmission of Covid-19, including information technology. Information technology developed to determine the shortest distance for Covid-19 cases around us needs to be developed. This research implements Dijkstra's Algorithm written in the React Native programming language to build a Covid-19 tracking application. The system can display the closest distance with a radius of at least one meter, and the test results can map the nearest radius of 41 meters and the most immediate radius of 147 meters. This system is built for the compatibility of Android OS and iOS applications with React Native programming.

Comparison of Machine Learning Algorithms for Classification of Drug Groups Purwono Purwono; Anggit Wirasto; Khoirun Nisa
SISFOTENIKA Vol 11, No 2 (2021): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/jst.v11i2.1134

The stages of clinical trials need to be carried out when determining a new drug group for patient management. This stage is considered quite long and requires a lot of money. Medical record system data continues to grow all the time. The data can be analyzed to find a pattern of grouping drugs used in the treatment of patients based on their body condition. Utilization of artificial intelligence (AI) technology can be done to classify drug data used during patient care. Machine learning as a branch of science in the AI field can be a solution to deal with these problems. Machines will learn, analyze, and predict drug requirements quickly with less cost. Based on related research, we contribute to comparing the performance of the best machine learning algorithms that can be used as drug classification models. The results of this study are the accuracy of the support vector machine algorithm is 94.7% while the random forest and decission tree algorithms are 98.2%. This shows that the algorithms that can be considered as a drug classification model are random forest and decision tree. This model needs to be tested on a larger dataset to produce the best accuracy value.

Analisa Cluster Data Transaksi Penjualan Minimarket Selama Pandemi Covid-19 dengan Algoritma K-means Iis Setyawan Mangku Negara; Purwono Purwono; Imam Ahmad Ashari
JOINTECS (Journal of Information Technology and Computer Science) Vol 6, No 3 (2021)
Publisher : Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jointecs.v6i3.2693

Covid-19 berdampak buruk pada sektor ekonomi di Indonesia. Hal ini terlihat dari kerugian yang dialami pelaku industri berupa penurunan omset penghasilan. Strategi penjualan perlu dilakukan agar kerugian dapat diminimalisir. Analisa transaksi penjualan bisa dilakukan untuk menemukan kelompok produk dengan data penjualan terbanyak sehingga manajemen stok dapat terpenuhi dan meningkatkan transaksi penjualan. Minimarket Berkah Abadi merupakan industri yang terdampak oleh pandemi ini. Analisa data belum dilakukan untuk mencari tahu produk mana yang memiliki data penjualan terbanyak, sehingga perlu dilakukan analisa dengan algoritma k-means. Algoritma ini dapat mengelompokan data berdasarkan kemiripan karakteristiknya. Penerapan algoritma pada 278480 data transaksi, didapatkan tiga cluster data penjualan yaitu cluster 2 atau penjualan terbanyak sebanyak 57 produk, cluster 1 atau penjualan sedang sebanyak 5 produk dan sisanya adalah cluster 0 dengan penjualan rendah. Hasil akurasi model klusterisasi yang dihasilkan dengan confusion matrix adalah 87%. Berdasarkan hasil ini pemilik Minimarket Berkah Abadi dapat terbantu dalam membuat keputusan pada manajemen stok barang pada saat pandemi Covid-19 masih berlangsung.

Klasifikasi Kinerja Programmer pada Aktivitas Media Sosial dengan Metode Stochastic Gradient Descent Rusydi Umar; Imam Riadi; Purwono Purwono
JOINTECS (Journal of Information Technology and Computer Science) Vol 5, No 2 (2020)
Publisher : Universitas Widyagama Malang

Kegagalan perusahaan pemula berbasis teknologi (startup) di Indonesia diakibatkan oleh kurang solidnya kinerja tim serta banyaknya kesalahan dalam proses rekrutmen programmer. Kemajuan pesat dalam budaya bermedia sosial dapat dimanfaatkan sebagai salah satu metode untuk memperoleh kandidat programmer terbaik dalam startup. Metode perekrutan yang digunakan dapat berupa melakukan proses klasifikasi konten media sosial kandidat programmer. Klasifikasi tersebut diharapkan dapat menemukan pola kinerja kandidat programmer, dengan hasil baik atau buruk. Metode klasifikasi yang dapat digunakan salah satunya adalah Stochastic Gardient Descent (SGD). Hasil klasifikasi menunjukkan nilai akurasi sebesar 80%, nilai precission 81% dan nilai recall 80%.

Strategi Gamifikasi Sebagai Peningkatan Motivasi Kuliah Pemrograman Website Pada Masa Pandemi Covid19 Purwono Purwono; Endang Setyawati; Khoirun Nisa; Amanah Wulandari
JOINTECS (Journal of Information Technology and Computer Science) Vol 6, No 3 (2021)
Publisher : Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jointecs.v6i3.2459

Pandemi Covid-19 masih menjadi wabah yang membahayakan manusia sehingga menjadi tantangan khusus dan menuntut adanya perubahan pada berbagai bidang masyarakat. Pendidikan menjadi salah satu bidang yang terdampak oleh pandemi ini. Aktivitas belajar mengajar harus dilakukan secara daring yang mengakibatkan kualitas belajar mengajar menjadi tidak maksimal. Perkuliahan daring sudah dilakukan lebih dari satu tahun. Model kuliah ini ternyata telah menurunkan motivasi belajar mahasiswa karena dianggap menjenuhkan sehingga dibutuhkan inovasi pembelajaran dari para Dosen. Gamifikasi menjadi salah satu solusi yang sudah beberapa kali digunakan dan merupakan suatu teknik yang dapat membuat mahasiswa berperan aktif selama perkuliahan serta merasa menikmati setiap tugas perkuliahan yang diberikan. Kami melakukan perancangan dan implementasi gamifikasi untuk matakuliah pemrograman website dengan platform classcraft. Hasil penelitian menunjukan adanya kenaikan motivasi mahasiswa dalam perkuliahan. Hal ini dilihat dari beberapa indikator yaitu tepat waktu dalam masuk kuliah naik dengan persentase 16.67%, minat diskusi dengan dosen naik 40%, ketepatan pengumpulan tugas naik 39,39%, kemampuan menyelesaikan projek UTS naik 28,57% dan kemampuan menyelesaikan projek UAS naik sebesar 34,21%.

Developing Data Integrity in an Electronic Health Record System using Blockchain and InterPlanetary File System (Case Study: COVID-19 Data) Imam Riadi; Tohari Ahmad; Riyanarto Sarno; Purwono Purwono; Alfian Ma'arif
Emerging Science Journal Vol 4 (2020): Special Issue "IoT, IoV, and Blockchain" (2020-2021)
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/esj-2021-SP1-013

The misuse of health data stored in the Electronic Health Record (EHR) system can be uncontrolled. For example, mishandling of privacy and data security related to Corona Virus Disease-19 (COVID-19), containing patient diagnosis and vaccine certificate in Indonesia. We propose a system framework design by utilizing the InterPlanetary File System (IPFS) and Blockchain technology to overcome this problem. The IPFS environment supports a large data storage with a distributed network powered by Ethereum blockchain. The combination of this technology allows data stored in the EHR to be secure and available at any time. All data are secured with a blockchain cryptographic algorithm and can only be accessed using a user's private key. System testing evaluates the mechanism and process of storing and accessing data from 346 computers connected to the IPFS network and Blockchain by considering several parameters, such as gas unit, CPU load, network latency, and bandwidth used. The obtained results show that 135205 gas units are used in each transaction based on the tests. The average execution speed ranges from 12.98 to 14.08 GHz, 26 KB/s is used for incoming, and 4 KB/s is for outgoing bandwidth. Our contribution is in designing a blockchain-based decentralized EHR system by maximizing the use of private keys as an access right to maintain the integrity of COVID-19 diagnosis and certificate data. We also provide alternative storage using a distributed IPFS to maintain data availability at all times as a solution to the problem of traditional cloud storage, which often ignores data availability. Doi: 10.28991/esj-2021-SP1-013 Full Text: PDF

Enkripsi Pesan Menggunakan Algoritma Linear Congruential Generator (LCG) dan Konversi Kode Morse Deny Nugroho Triwibowo; Purwono; Imam Ahmad Ashari; Arif Setia Sandi; Yusuf Fadlila Rahman
Buletin Ilmiah Sarjana Teknik Elektro Vol. 3 No. 3 (2021): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v3i3.5546

Message is an expression that is formed from a thought and feeling, reality, opinion, experience that has happened or will come, and so on. Currently, the exchange of information carried out by both the sender and the recipient has used several social media applications, such as whatsapp, telegram, line, and many more. Messages sent and received through social media applications must be connected to the internet network for the communication process in which the message can be stolen by irresponsible parties, because these parties are also connected to the same internet network. One way that can be done so that the message sent is maintained and more secure, the process of securing the data and information contained in the message is carried out. The Linear Congruential Generator (LCG) algorithm will generate a random key value and convert it into Morse code form. The results obtained from the modification of the LCG algorithm and Morse code are very helpful in the encryption process which makes the encryption results obtained quite difficult to solve because in the encryption process one plaintext character is replaced with a dot sign (.) and a minus sign (-) on the ciphertext. Pesan merupakan ungkapan yang terbentuk dari suatu pemikiran dan perasaan, realita, opini, pengalaman yang sudah terjadi atau yang akan datang, dan lain sebagainya. Saat ini pertukaran informasi yang dilakukan baik pengirim dan penerima sudah menggunakan beberapa aplikasi media sosial, seperti whatsapp, telegram, line, dan masih banyak lagi. Pesan yang dikirim dan diterima melalui aplikasi media sosial harus terhubung dengan jaringan internet untuk proses komunikasinya yang di mana pesan tersebut dapat dicuri oleh pihak yang tidak bertanggungjawab, dikarenakan pihak-pihak tersebut juga terhubung pada jaringan internet yang sama. Salah satu cara yang dapat dilakukan agar pesan yang dikirimkan terjaga dan lebih aman, dilakukan proses pengamanan data dan informasi yang ada didalam pesan tersebut. Algoritma Linear Congruential Generator (LCG) akan menghasilkan nilai kunci secara acak dan dikonversi ke dalam bentuk kode morse. Hasil yang didapatkan dari modifikasi algoritma LCG dan konversi kode morse dapat membantu untuk proses kriptografi yang membuat hasil enkripsi pesan cukup susah untuk dibaca karena dalam proses enkripsi pesan tiap-tiap karakter plaintext akan dikonversikan dengan tanda titik (.) dan tanda kurang (-) pada cipherteksnya.

Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model Purwono Purwono; Alfian Ma'arif; Iis Setiawan Mangku Negara; Wahyu Rahmaniar; Jihad Rahmawan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 3 (2021): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i3.22237

Stroke is a disease caused by brain tissue damage because of blockage in the cerebrovascular system that disrupts body sensory and motoric systems Stroke disease is one of the highest death cause in the world. Data collection from Electronic Health Records (EHR) is increasing and has been included in the health service big data. It can be processed and analyzed using machine learning to determine the risk group of stroke disease. Machine learning can be used as a predictor of stroke causes, while the predictor clarifies the influence of each cause factor of the disease. Our contribution in this research is to evaluate Feyn Qlattice machine learning models to detect the influence of stroke disease's main cause features. We attempt to obtain a correlation between features of the stroke disease, especially on the gender as a feature, whether any other features can influence the gender feature. This research utilizes 4908 data of the disease predictor using the Feyn Qlattice model. The result implies that gender highly impacts age and hypertension on stroke disease causes. Autorun in Feyn Qlattice model was run with ten epochs, resulting in 17596 test models at 57s. Query string parameter that was focused on age and hypertension features resulted in 1245 models at 4s. An increase of accuracy was found in training metrics from 0.723 to 0.732 and in testing metrics from 0.695 to 0.708. Evaluation results showed that the model is reasonably good as a predictor of stroke disease, indicated with blue lines of AUC in training and testing metrics close to ROC's left side peak curve.

Model Prediksi Kualitas Udara dengan Support Vector Machines dengan Optimasi Hyperparameter GridSearch CV Ahmad Toha; Purwono Purwono; Windu Gata
Buletin Ilmiah Sarjana Teknik Elektro Vol. 4 No. 1 (2022): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v4i1.6079

Air pollution continues to increase in Jakarta. The city ranks 12th in the world as the capital of a country with high levels of pollution. The Jakarta Environmental Service requires processing air quality data generated by the Air Quality Monitoring Station in order to produce valuable information as a decision-making tool. This data processing can be processed with data mining techniques to seek new knowledge from the database so as to find valid, useful and easy-to-learn patterns. The SVM data mining classification model is proposed in this study. Our contribution in this research is to create a classification model with SVM with new techniques, namely improvements in data processing to perform hyperparameter tuning. We saw that previous researchers only pursued high accuracy scores. In contrast to previous studies, we used the gridsearch cv hyperparameter optimization technique on the SVM classification model. The kernel polynomial with 2 degrees is the best parameter recommendation from the grid search cv technique. The accuracy before optimization is 73,31%, while after optimization is 94,8%. This shows an increase in accuracy of 3.2% after applying the grid search cv method to the classification of air quality monitoring using the SVM model Pencemaran udara terus meningkat di Jakarta. Kota ini menempati urutan ke 12 di dunia sebagai ibukota negara dengan tingkat polusi tinggi. Dinas Lingkungan Hidup Jakarta memerlukan pengolahan data-data kualitas udara yang dihasilkan oleh Stasiun Pemantauan Kualitas Udara agar menghasilkan informasi berharga sebagai alat pengambil keputusan. Pengolahan data ini dapat diproses dengan teknik data mining untuk mencari pengetahuan baru dari basis data sehingga menemukan pola-pola yang valid, bermanfaat dan dapat dipelajari dengan mudah. Model klasifikasi data mining SVM diusulkan dalam penelitian ini. Kontribusi kami dalam penelitian ini adalah membuat model klasifikasi dengan SVM dengan teknik baru yaitu perbaikan dalam pemrosesan data hingga melakukan hyperparameter tuning. Kami melihat para peneliti sebelumnya hanya mengejar nilai akurasi yang tinggi. Berbeda dengan penelitian sebelumnya, kami menggunakan teknik optimasi hiperparameter gridsearch cv pada model klasifikasi SVM. Polinomial kernel dengan 2 derajat merupakan rekomendasi parameter terbaik dari teknik grid search cv. Akurasi sebelum optimasi adalah 73,31%, sedangkan setelah optimasi adalah 94,8%. Hal ini menunjukkan peningkatan akurasi sebesar 21,5% setelah menerapkan metode grid search cv pada klasifikasi pemantauan kualitas udara menggunakan model SVM.

Perbandingan Metode SVM, RF dan SGD untuk Penentuan Model Klasifikasi Kinerja Programmer pada Aktivitas Media Sosial Rusydi Umar; Imam Riadi; Purwono
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 2 (2020): April 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

Co-Authors Agung Budi Prasetio Agung Pangestu Ahmad Toha Alfian Ma’arif Amanah Wulandari Anggit Wirasto Anggit Wirasto Ariefah Khairina Islahati Arif Setia Sandi A. Asmat Burhan Asmat Burhan Bala Putra Dewa Bala Putra Dewa Bala Putra Dewa Bala Putra Dewa Barlian Kristanto Burhanuddin bin Mohd Aboobaider Deny Nugroho Triwibowo Dewi Astria Faroek Dimas Febri Kuncoro Dimas Herjuno Eko Ariyanto Elsa Wulandari, Annastasya Nabila Endang Setyawati Fakhri Zahi Mumtaza Hadi Jayusman Hesti Ayu Wahyuni Iin Dyah Indrawati Iis Setiawan Mangkunegara Iis Setyawan Mangku Negara Imam Ahmad Ashari Imam Ahmad Ashari, Imam Ahmad Imam Riadi Imam Riadi Jatmiko Indriyanto Jihad Rahmawan Khoirun Nisa Khoirun Nisa Khoirun Nisa Lutviana Lutviana Lutviana Mangku Negara, Iis Setiawan Mangkunegara, Iis Setiawan Marlia Hafny Afrilies Maya Ruhtiani Muchammad Naseer Muhammad Baballe Ahmad Muhammad Haikal Satria Muntiari, Novita Ranti Musafa Widagdo Nandang Hermanto Pramesti Dewi Qazi Mazhar ul Haq Rahmadhani, Berlina Riska Suryani Riyanarto Sarno Rosyid Ridlo Al-Hakim Rubaeah, Siti Rusydi Umar Safa Kiana Safar Dwi Kurniawan Sandi Najib Iskandar Sharkawy , Abdel-Nasser Slamet Slamet Sony Kartika Wibisono Sony Kartika Wibisono Sony Kartika Wibisono Sony Kartika Wibisono Supriyatin Supriyatin Tohari Ahmad Tusaria Tri Wahyu Ningrum Wahyu Rahmaniar Windu Gata Yanuar Zulardiansyah Arief Yudhistira , Aimar Yuris Tri Naili Yuris Tri Naili Yuslena Sari, Yuslena Yusuf Fadlila Rahman

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