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All Journal Jurnal Teknologi dan Manajemen Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI INTEK: Informatika dan Teknologi Informasi Jurnal Inovasi Informatika Prosiding Konferensi Nasional PKM-CSR
Handri Santoso
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Nursing Public Health Social Sciences Other

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Plat Nomor Kendaraan dengan Convolution Neural Network Djarot Hindarto; Handri Santoso
Jurnal Inovasi Informatika Vol. 6 No. 2 (2021): Jurnal Inovasi Informatika
Publisher : Universitas Pradita

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51170/jii.v6i2.202

Abstract

The development of Deep Learning technology is very good at detecting Objects. One of them is detection on the vehicle number plate. This method can be applied to Computer Vision to process images using DensetNet121, NasNetLarge, VGG16 and VGG19 methods. The most basic difference between Machine Learning and Deep Learning is the inclusion of a Hidden Layer and what distinguishes the Deep Learning process using neurons as a process from input, process to output. Feature extraction is done directly with the Deep Learning process. In terms of time, training models with Deep Learning are very long, when compared to Machine Learning. The dataset comes from Kaggle, then training is carried out with four Deep Learning models, resulting in a model. There are differences in conducting the training process. Before carrying out the Training process, a pre-paration process from the Image Dataset is carried out. The dataset is divided into two parts, the Training Dataset and the Testing Dataset. After the training model is completed, it is continued with the Testing process and measuring the performance of the model's accuracy. The accuracy of the four models resulting from Deep Learning training is also presented
Evaluasi tata kelola IT menggunakan Framework COBIT terhadap pengaruh kinerja di Rumah Sakit Erik Aditya Gunawan; Handri Santoso; Richardus Eko Indrajit
Jurnal Inovasi Informatika Vol. 7 No. 1 (2022): Jurnal Inovasi Informatika
Publisher : Universitas Pradita

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51170/jii.v7i1.224

Abstract

Di semua bidang usaha pada saat ini, perkembangan era digitalisasi sangat lah mempengaruhi bisnis proses di dunia rumah sakit dalam memberikan pelayanan. Khususnya dalam hal ini pemberian layanan berbasis digital menjadi peran utama rumah sakit dalam memberikan kebutuhan para pasien agar terselenggara penyediaan kesehatan dapat memenuhi kebutuhan dan arah tujuan dari rumah sakit, yaitu adalah peningkatan efektifitas layanan dan juga efisiensi layanan. Agar dapat terselenggaranya layanan kesehatan berbasis digital yang baik, tentunya dibutuhkan juga tata kelola IT yang baik agar dapat menjaga keberlangsungan penyediaan layanan berbasis digital. Pada kesempatan ini, peneliti akan mencari hubungan bagaimana evaluasi tata kelola yang baik dengan menilai kematangan dari proses tata kelola IT dengan menggunakan metode COBIT. Sehingga diharapkan dari evaluasi yang dilakukan dapat menjadi acuan dasar bagi management dalam pengelolaan dan pembenahan tata kelola IT agar selaras dengan kebutuhan perusahaan dalam menjalankan bisnisnya khususnya di dunia kesehatan yang berbasis digital. Pada penelitian yang dilakukan, dari tingkat kematangan yang ada, dievaluasi dari GAP yang menjadi standar minimal pelayanan bagi rumah sakit terhadap capaian indicator yang diharapkan dari proses yang ada khususnya dalam kematangan dari tata kelola IT yang diharapkan.
Perbandingan Model Deep Learning untuk Klasifikasi Sentiment Analysis dengan Teknik Natural Languange Processing Firman Pradana Rachman; Handri Santoso
Jurnal Teknologi dan Manajemen Informatika Vol 7, No 2 (2021): Desember 2021
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v7i2.6506

Abstract

Everyone has an opinion or opinion on a product, public figure, or government policy that is spread on social media. Opinion data processing is called sentiment analysis. In processing large opinion data, it is not enough to only use machine learning, but you can also use deep learning combined with NLP (Natural Language Processing) techniques. This study compares several deep learning models such as CNN (Convolutional Neural Network), RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory), and several variants to process sentiment analysis data from Amazon and Yelp product reviews.
Komparasi Performansi Algoritma Naive Bayes dan Logistic Regression pada Malware Android Andreas Putra Wijaya; Handri Santoso
INTEK : Jurnal Informatika dan Teknologi Informasi Vol. 4 No. 2 (2021)
Publisher : Universitas Muhammadiyah Purworejo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37729/intek.v4i2.1558

Abstract

Currently, Indonesian people have used Internet technology for various needs. Starting from transportation, shopping to the world of education using the Internet. Equipment in accessing the Internet varies, ranging from computers, laptops to communication devices such as mobile devices. Currently, mobile devices that are quite widely used by the public are mobile devices based on the Android operating system. In this situation it encourages certain parties to take advantage of loopholes to seek profit, one of which is the creation of Malware. In addition, developments in the field of artificial intelligence are currently very advanced and encourage many researches in various fields to use it. This situation makes researchers focus on malware analysis by utilizing artificial intelligence technology. The purpose of this study is to analyze Android APK files by classifying the Malware family. Performance and accuracy measurements will also be presented in a comparison between the Naïve Bayes algorithm and the Logistic Regression algorithm. The method used is Supervised Learning classification, using Naïve Bayes algorithm and Logistic Regression. Everywhere both methods are Machine Learning algorithms and part of artificial intelligence.
PENINGKATAN LITERASI DIGITAL MASYARAKAT TERHADAP SOCIAL ENGINEERING DALAM MASA PANDEMI COVID-19 Theresia Herlina Rochadiani; Handri Santoso; Dennis Anthony Plaudo; Richard Setiawan; Vincensius Gilven Fiones
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 4 (2021): Peran Perguruan Tinggi dan Dunia Usaha dalam Mewujudkan Pemulihan dan Resiliensi Masya
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (320.429 KB) | DOI: 10.37695/pkmcsr.v4i0.1151

Abstract

Seluruh dunia, tak terkecuali Indonesia mengalami pandemi Covid-19 dari tahun 2020 sampai saat ini. Dengan adanya pandemi Covid-19 menyebabkan perubahan pola hidup masyarakat yang cenderung semakin banyak melakukan pekerjaan, kegiatan, dan transaksi secara daring daripada luring. Hal ini memicu semakin banyaknya kejahatan siber. Kejahatan siber di Indonesia masuk sebagai peringkat ke-2 di dunia. BSSN juga menyebutkan bahwa pada tahun 2020, dalam masa pandemi ini, kejahatan siber naik 2 kali lipat dibandingkan tahun 2019. Dalam sistem jaringan komputer, manusia adalah komponen yang terlemah sehingga para penjahat siber memafaatkan hal ini dengan menggunakan social engineering, yaitu manipulasi psikologis korban, dalam melakukan kejahatan siber. Rendahnya literasi digital masyarakat akan keamanan siber membuat banyak masyarakat yang menjadi korban. Oleh karena permasalahan tersebut, kegiatan PkM ini bertujuan untuk meningkatkan literasi digital masyarakat, khususnya akan social engineering, sehingga dapat menekan jumlah kejahatan siber. Edukasi kepada masyarakat mengenai social engineering ini dilakukan dengan membuat video edukasi contoh-contoh social engineering beserta mitigasinya dan kemudian didiseminasikan secara daring. Melalui survey yang dilaksanakan sebelum dan sesudah edukasi dapat dilihat adanya peningkatan literasi digital masyarakat akan social engineering, yaitu terjadi peningkatan dengan rata-rata persentase peningkatan dari 3 video edukasi sekitar 63.29%.
PENERAPAN TEKNOLOGI IOT DALAM MEMBANTU PEMANTAUAN KUALITAS AIR KOLAM PETERNAK IKAN Theresia Herlina Rochadiani; William Widjaja; Handri Santoso; Youzy Natasya; Ulfah Dzakiyah Nisrina Ariqoh; Regina Angelika Septi Rahayu
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 5 (2022): PERAN PERGURUAN TINGGI DAN DUNIA USAHA DALAM AKSELERASI PEMULIHAN DAMPAK PANDEMI
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37695/pkmcsr.v5i0.1789

Abstract

Ikan hias menjadi salah satu barang komoditi yang cukup menjanjikan. Dan Indonesia merupakan negara eksportir terbesar keempat di dunia pada tahun 2016-2019. Meskipun ikan hias menawarkan keuntungan bisnis yang menggiurkan, para peternak ikan hias sering mengalami gagal panen dikarenakan tidak terpantaunya kualitas air sebagai habitat ikan hias. Hal ini juga dialami peternak ikan hias di Kampung Kalipaten. Oleh karena itu, melalui kegiatan pengabdian masyarakat ini, dibangun teknologi IoT yang dapat membantu peternak ikan dalam memantau kualitas air berdasar nilai ph, tds, dan suhu air. Kegiatan ini diawali dengan tahap identifikasi masalah melalui wawancara dengan peternak ikan. Setelah mengetahui bahwa permasalahan yang dihadapi peternak ikan adalah kurang terpantaunya kualitas air yang menyebabkan gagal panen, maka tahap selanjutnya yang dilakukan adalah merancang sistem IoT berbasis LoRaWAN untuk memantau kualitas air kolam ikan. Berdasarkan rancangan tersebut, tahap pembangunan sistem IoT dilakukan dan juga dibangun aplikasi berbasis mobile yang dapat digunakan oleh peternak ikan untuk memantau dari mana saja dan kapan saja. Melalui kerja sama yang baik antar tim pelaksana kegiatan masyarakat dan peternak ikan, maka sistem IoT dengan menggunakan microcontroller Heltec ESP32, sensor ph, sensor tds, dan sensor suhu DS18B20, gateway LoRa beserta aplikasi berbasis mobile berhasil dibangun.
Transfer Gaya Gambar Batik Menggunakan Neural Style Transfer dan Convolutional Autoencoder Celvyn Yulian; Handri Santoso; Ito Wasito; Haryono .
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 4 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i4.5573

Abstract

The challenge of neural networks to process visual art judgments like humans inspired Gatys et al and in 2015 they succeeded in creating neural style transfer (NST) that can transfer European artistic image styles to other images. At present, research related to NST has been widely conducted, but its use with a convolutional autoencoder (CAE) as one of the NN architectures capable of compressing NST output is still rare. This research intends to design an NST system with CAE as an additional architecture in charge of the compression process while maintaining the force transferred. As a substitute for European-style artistic images, batik is used as an original Indonesian artistic work. NST and compression images will be measured using structural similarity index measure (SSIM) evaluation metrics. The evaluation results showed that the system designed managed to get an average SSIM score of 0.67 out of 1 and an average value of storage size reduction ratio of 37.43% from the original size. Then, the survey showed that the quality of the compressed image was quite good with a score of 64.09% and the compressed image was quite usable in the field of work of each respondent with a score of 49.09%.
Sistem Pengenalan Emosi Menggunakan Autoencoder + CNN + Attention Mikhail Aresa Latumahina; Handri Santoso; Ito Wasito; Haryono .
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 4 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i4.5576

Abstract

In the Digital Transformation era, many businesses use technology in the form of Deep Learning which is used to change the way business is run, one of the methods used is Emotion Recognition. Emotion Recognition itself is part of Computer Vision, and computer vision tasks are usually done using the CNN algorithm. Accuracy is important in Emotion Recognition where many studies use various methods, both Transfer and Hybrid learning to try to improve this aspect, so this research intends to design a Autoencoder + CNN + Attention that can be used for Emotion recognition, which is made by combining Encoder, CNN, and Attention Mechanisms. this model is circumspect by using FER2013 and compared to the CNN + Attention model which is shutting down in the same way. Even though the Autoencoder + CNN + Attention managed to get 64% Accuracy in Evaluate Test_Model compared to CNN + Attention which got 55%, it should be noted that adjustments still have to be treated because of the 43% sensitivity of testing on external data such as tuning, layer adjustments, and FER2013 data augmentation.
Zero Knowledge Proof for SNAP (Standar Nasional OPEN API Pembayaran) in Indonesia Ramadhoni, Moehammad; Handri Santoso
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12423

Abstract

SNAP (Standar Nasional OPEN API Pembayaran) is an implementation of open banking for encouraging digital transformation in the banking industry. SNAP was submitted by several sub-working groups formed jointly by ASPI and the Bank of Indonesia. In the document Pedoman Tata Kelola (Bank of Indonesia, n.d.), there is already a customer data protection mechanism between the bank, the owner of Open API, and the user of Open API. However, there is no data protection process carried out by consumers so third parties, that use the Open API of the bank, do not need to know the customer's data. Based on the web3 protocol, users can store data and transmit only in encrypted form which can only be opened by calculating the data with a pre-agreed smart contract. Banks can work like a decentralized network on web3, where the process of calculating proof and witness is carried out by the bank. Proof and witness are calculated using a zero-knowledge proof protocol, making it difficult to duplicate. For this reason, we propose a new architecture using smart contracts between banks and customers using the ZK-SNARK method. Therefore, there is no significant performance difference between using ZK-SNARK and without ZK-SNARK in the API call process.
Co-Authors Andreas Putra Wijaya Celvyn Yulian Dennis Anthony Plaudo Djarot Hindarto Firman Pradana Rachman Haryono . Ito Wasito Mikhail Aresa Latumahina R. Aditya Gunawan, R. Aditya Ramadhoni, Moehammad Regina Angelika Septi Rahayu Richard Setiawan Richardus Eko Indrajit Theresia Herlina Rochadiani Ulfah Dzakiyah Nisrina Ariqoh Vincensius Gilven Fiones William Widjaja Youzy Natasya