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All Journal TEKNIK INFORMATIKA Jurnal Ilmiah Informatika Komputer VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro Network Engineering Research Operation [NERO] JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Komtika (Komputasi dan Informatika) Jiko (Jurnal Informatika dan komputer) Multitek Indonesia : Jurnal Ilmiah KOMPUTEK bit-Tech Pilar Teknologi : Jurnal Penelitian Ilmu-ilmu Teknik Jurnal Ilmiah Edutic : Pendidikan dan Informatika Formosa Journal of Computer and Information Science Kenduri: Jurnal Pengabdian Dan Pemberdayaan Masyarakat Jurnal ilmiah teknologi informasi Asia Mathematics, Informatics, Knowledge, And Information Research
Cobantoro, Adi Fajaryanto
Universitas Muhammadiyah Ponorogo
Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Mechanical Engineering Social Sciences

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PEMBERIAN PRIORITAS PENAMBAHAN GURU SEKOLAH MENENGAH ATAS DI KABUPATEN MAGETAN DENGAN METODE WEIGHTED SUM MODEL Novia Anggraini; Adi Fajaryanto Cobantoro; Fauzan Masykur
KOMPUTEK Vol 6, No 1 (2022): April
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/jkt.v6i1.1146

Abstract

Education is one thing that is very mandatory for citizens, especially Indonesia. Apart from clothing, food, shelter, and health, which are other basic needs, education is no less important. The number of teachers is too few to cater for the large number of students, so more teachers are needed. This study aims to give priority to the addition of public high school teachers in Magetan Regency with the Weighted Sum Model method. This research is only limited to being done with a Decision Support System using the Weighted Sum Model method. This research was conducted by collecting data on state high schools in Magetan Regency, East Java Province which was used as an alternative data system and then designing applications using the php and mysql programming languages. This research produces a web- based Expert System Application that implements a Decision Support System with the Weighted Sum Model method. From testing the functionality of the system, the improvement of public high school teachers in Mageten Regency by utilizing the Weighted Sum Model method has succeeded well by 100. 
INTEGRASI ALGORITMA FISHER-YATES SEBAGAI PENGEMBANGAN E-LEARNING DI UNIT KEGIATAN BELAJAR MANDIRI Karisma Nanda Arditya; Moh Bhanu Setyawan; Adi Fajaryanto Cobantoro
KOMPUTEK Vol 5, No 1 (2021): April
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/jkt.v5i1.684

Abstract

E-learning is currently very important and a must for educational institutions. Many e-learning application platforms are based on Learning Management System (LMS) that can be used by schools, whether they are open source, paid for or build their own applications. There are many considerations that must be faced when deciding to use an e-learning-based application (LMS) including infrastructure readiness, costs and application compatibility with the e-learning concept desired by educational institutions. The availability of features in the e-learning system is one that must be adjusted to the e- learning concept that will be selected. Each institution may be able to determine its own characteristics that can be specific and not yet fully accommodated in the e-learning application that currently exists. One of them is the feature to randomize exam questions to ensure that each student gets different questions with the same composition. This research will try to answer this problem by integrating the fisher-yates shuffel algorithm. The success of this algorithm integration will be assessed by conducting an exam simulation involving two classes and seeing how the system will randomly divide the exam questions to each student. The first assessment is the suitability of the composition of the questions and the similarity of the questions between students, the less students receive the same questions appearing on the exam questions, the better the system performance.
Analisis Implementasi Teknologi Pembelajaran di Bebas UMPO Elok Putri Nimasari; Adi Fajaryanto Cobantoro; Sony Dwiki Andika; Moh. Bhanu Setyawan
EDUTIC Vol 9, No 2: Mei 2023
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v9i2.19938

Abstract

Kemajuan ICT (Information Communication and Technology) telah memberi pengaruh besar dalam aspek pendidikan. Salah satu pemanfaatan ICT dalam bidang pendidikan era digital adalah e-learning. Pada hasil penelitian sebelumnya, e-learning memiliki banyak kemudahan dalam proses pembelajaran, dianalisis dari perspektif mahasiswa dan dosen. Moodle merupakan platform e-learning yang paling populer karena memiliki banyak fitur yang memudahkan pengguna dalam mengakses pembelajaran jarak jauh. Pada salah satu universitas swasta di kota Ponorogo, Indonesia, platform e-learning Moodle telah dimodifikasi dan dikembangkan sesuai dengan kebutuhan berbasis kurikulum perguruan tinggi bernama BEBAS UMPO. Platform ini telah mewadahi keseluruhan mata kuliah dan kelas sertifikasi online untuk mahasiswa selama 3 tahun terakhir. Penelitian ini bertujuan untuk menganalisis kemandirian belajar mahasiswa dalam implementasi teknologi pembelajaran di BEBAS UMPO, khususnya pada program sertifikasi Bahasa Inggris online (STAcEP). Metode mix methods digunakan sebagai metodologi penelitian dengan 3 instrumen penelitian yaitu wawancara, angket, dan observasi. Instrumen wawancara dan observasi divalidasi melalui studi pustaka, sementara angket diuji melalui uji validitas dan reliabilitas. Hasil penelitian menunjukkan tingkat kemandirian belajar mahasiswa dalam kelas e-learning STAcEP tergolong dalam kategori “baik” dengan hasil kriteria peniliaian di interval 70%. Hasil ini didukung oleh hasil wawancara dan observasi yang mendalam. Penelitian selanjutnya diharapkan dapat memperluas sampel yang diteliti tidak hanya satu fakultas saja agar data yang diperoleh hasil yang lebih valid dan dapat digeneralisir. Selain itu, fokus penelitian bisa diperluas dalam aspek usability dan user experience.
Rekayasa Aplikasi Eposal Menggunakan Algoritma Base64 Untuk Menyimpan Data Pengguna Adi Fajaryanto Cobantoro; Mohammad Bhanu Setyawan; Hardiyan Oktavianto
Jurnal Komtika (Komputasi dan Informatika) Vol 7 No 1 (2023)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v7i1.8711

Abstract

E-commerce merupakan kegiatan jual beli yang menggunakan media internet. Kerap ditemukan adanya data pribadi pelanggan yang bersifat rahasia seperti nama lengkap, alamat dan nomor telepon tersimpan pada database E-commerce. Selain itu data-data credential lain juga sering kali bocor pada aplikasi berbasis internet. Kebocoran data dapat disebab oleh, konfigurasi perangkat lunak yang salah, social engineering, Recycled Passwords, Physical Theft of Sensitive Devices, Software Vulnerabilities, dan Use of Default Passwords. Banyak kasus kebocoran data di Indonesia disebabkan oleh konfigurasi perangkat lunak yang salah, sehingga untuk mengamankan data harus memiliki keahlian dibidang keamanan. Salah satu langkah pencegahan kebocoran data adalah Encrypt All Data. Yang dimaksud Encrypt All Data disini adalah mengenkripsi semua data yang ada di dalam database. Metode enkripsi tersebut salah satunya adalah Algoritma Base64. Algoritma Base64 merupakan algoritma yang menggunakan kode ASCII dalam proses encoding maupun decodingnya. Pada proses Enkripsi dan Dekripsi, Algoritma Base64 menggunakan dua buah tabel bantu yaitu tabel ASCII dan tabel index Base64. Pada tahap awal dilakukan proses perubahan kata menjadi kode ASCII. Tahap kedua, kode ASCII tersebut akan diubah ke dalam kode biner 8bit. Tahap ketiga, kode biner 8bit dibagi menjadi kode biner 6 bit. Tahap keempat, blok-blok tersebut dikembalikan lagi ke bentuk desimal, kemudian disesuaikan dengan tabel Index Base64. Sedangkan untuk proses dekripsi, merupakan kebalikan dari proses enkripsi dengan proses yang sama. Tahap kedua, dilakukan perubahan dari kode Index ke dalam kode biner 6. Tahap ketiga, membuat kode biner 6bit menjadi kode biner 8bit. Tahap keempat yaitu mengubah biner 8 ke ASCII. Tahapan selanjutnya adalah mengubah kode ASCII ke kode desimal. Alur algoritma Base64 pad apenelitian ini akan diimplementasikan pada aplikasi Eposal di Toko Mina Alumunium. Proses implementasi ini dengan menambahkan satu fungsi “base64_encode” untuk setiap data yang masuk ke dalam database. Fungsi tersebut dimasukkan kedalam salah satu proses yang ada pada aplikasi Eposal yaitu proses simpan data konsumen Mina Alumunium. hasil yang diperoleh adalah bahwa setiap data yang dimasukkan ke dalam Eposal Mina Alumunium atau karakter yang diinputkan tersebut disimpan didalam database berbentuk enkripsi data acak. Sehingga jika ada penyusup yang berhasil masuk ke dalam database, penyusup tersebut tidak bisa membaca data yang ada di dalam database.
ERFORMANCE ANALYSIS OF ALEXNET CONVOLUTIONAL NEURAL NETWORK (CNN) ARCHITECTURE WITH IMAGE OBJECTS OF RICE PLANT LEAVES Adi Fajaryanto Cobantoro; Fauzan Masykur; Kelik Sussolaikah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 8 No. 2 (2023): JITK Issue February 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1342.986 KB) | DOI: 10.33480/jitk.v8i2.4060

Abstract

Rice is a staple food consumed by Indonesian people, even 75% of the world's population consumes rice and it is mostly found in Asia. Rice derived from pounded rice is a staple food so it can be consumed. In the process of planting rice, pests and diseases are not spared so that it can affect crop yields. Pest and disease attacks need fast, accurate and precise handling so that crop failures. In this paper, we will discuss the classification of leaf diseases of rice plants using the Convolutional Neural Network (CNNN) algorithm, especially the Alexnet architecture. There are 4 types of disease, namely Brown spot, Leafblast, Hispa and Healthy. Models built based on the Alexnet architecture may have differences in the level of accuracy and loss compared to other architectures due to the different stages in the sequential model formation. The dataset used is public data from Kaggle consisting of 4 classes with a total of 1,600 images. In each class the dataset is divided for training, testing and validation datasets with a ratio of 70:20:10. As for tools in the process of training datasets using Google Colab from Google. After going through the stages of the research, the research results obtained are accuracy worth 99,22%, mean average precision worth 0,24 and loss worth 0,05.
Otomasi Greenhouse Berbasis Mikrokomputer RASPBERRY PI Adi Fajaryanto Cobantoro; Mohammad Bhanu Setyawan; Miftahudin Agung Budi Wibowo
Jurnal Ilmiah Teknologi Informasi Asia Vol 13 No 2 (2019): Volume 13 Nomor 2 (8)
Publisher : LP2M INSTITUT TEKNOLOGI DAN BISNIS ASIA MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v13i2.360

Abstract

The entry of the industrial revolution era, many occur anthropogenic. One of the causes of ecological imbalances is the lack of reforestation in urban environments. Environmental conditions are the main thing to achieve production. The main factors that influence plant growth and development are temperature and humidity, in this case to reach the desired temperature and humidity is very difficult and difficult to control as needed. As if the temperature and humidity limit agricultural production. From the existing problems, building a greenhouse prototype can automatically control temperature and humidity according to the actual conditions in the plant. To achieve this condition use a control system that controls temperature and humidity automatically. The system works according to the value that has been determined then the value compared with the DHT22 sensor to measure air humidity and YL-69 as a controller of soil moisture and as a controller for watering plants automatically. The prototype testing was done using a computer and raspberry pi microcontroller by connecting the UTP cable to the raspberry pi to the laptop with an internet sharing connection. The prototype can run and can be controlled by telegram.
HARDENING SERVER MENGGUNAKAN METODE PORT KNOCKING PADA SISTEM PROGRAM STUDI TEKNIK INFORMATIKA UNIVERSITAS MUHAMMADIYAH PONOROGO Muhammad Reza; Adi Fajaryanto Cobantoro; Ismail Abdurrozzaq Zulkarnain
Jurnal Ilmiah Informatika Komputer Vol 29, No 3 (2024)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2024.v29i3.12954

Abstract

Pada era digitalisasi, keamanan data menjadi sangat rentan terhadap ancaman kebocoran, khususnya pada server yang menyimpan informasi sensitif. Penelitian ini bertujuan meningkatkan keamanan server Program Studi Teknik Informatika Universitas Muhammadiyah Ponorogo melalui implementasi hardening server. Langkah-langkah yang diterapkan meliputi konfigurasi port knocking untuk otentikasi akses, pengaturan firewall iptables, aktivasi portsentry, pemanfaatan Snort sebagai Intrusion Detection System (IDS), dan pemblokiran permintaan ICMP guna menangkal serangan berbasis ping. Pengujian keamanan menggunakan alat audit Lynis menunjukkan peningkatan signifikan, dengan skor keamanan awal 65, yang menunjukkan kerentanan tinggi, meningkat menjadi 96 setelah implementasi hardening. Penelitian ini menghadirkan pendekatan baru dengan mengintegrasikan berbagai mekanisme keamanan secara simultan, termasuk kombinasi port knocking dengan IDS Snort. Pendekatan ini memberikan perlindungan lebih baik terhadap risiko akses tidak sah, yang jarang diterapkan secara bersamaan dalam penelitian serupa. Langkah-langkah utama mencakup pembaruan sistem berkala, perlindungan port SSH (port 22) melalui pengaturan firewall, serta uji urutan otentikasi port knocking yang terintegrasi dengan IDS. Evaluasi dilakukan secara berulang menggunakan Lynis untuk mengukur efektivitas setiap langkah. Hasil penelitian membuktikan bahwa metode ini mampu meningkatkan ketahanan sistem secara substansial, menjaga kerahasiaan, integritas, dan ketersediaan data. Dengan demikian, server Program Studi Teknik Informatika menjadi lebih kuat dalam menghadapi ancaman siber.
Identifikasi Performa Algoritma Fuzzy Mamdani Sebagai Kendali Proses Koagulasi pada Internet of Thing Pembuatan Tahu Yovi Litanianda; David April Riyanto; Angga Prasetyo; Adi Fajaryanto Cobantoro; Ismail Abdurrozaq Zulkarnain
bit-Tech Vol. 7 No. 2 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i2.1972

Abstract

Proses pembuatan tahu dilakukan dalam berapa tahapan. Tahapan terpenting dalam pembuatan tahu yakni terletak pada proses penggumpalan (koagulasi) sari kedelai yang telah direbus. Pada tahapan ini bayak faktor yang menentukan keberhasilannya, diantaranya suhu sari kedelai, PH cuka sebagai katalis reaksi koagulasi dan kecepatan pengadukan. Jika terjadi ketidak sesuaian salah satunya maka akan berakibat sari kedelai gagal menggumpal dan terbuang. Produksi tahu yang masih tradisional membuat pekerjaan ini masih mengandalkan kepiawaian pekerja senior yang terampil. Ketergantungan pada keterampikan pekerja akan menghambat keberlangsungan industry. Untuk mengatasi masalah tersebut, dicoba dikembangkan perangkat IoT yang mampu mengendalikan proses koagulasi pada pembuatan tahu. Sistem ini bekerja berdasarkan algoritma Fuzzy Mamdani yang akan mengolah input nilai suhu sari kedelai dan nilai PH cuka menjadi nlai PWM yang menjadi penentu kecepatan motor pengaduk larutan sari kedelai. Tingkat keberhasilan algoritma fuzzy menangani kondisi nyata yang bervariasi menjadi ukuran performanya. Pengujian dilakukan dengan sekenario menguji lansung dengan kondisi nyata sari kedelai dan cuka untuk diketahui tingkat keberhasilannya dalam melakukan pengendalian proses koagulasi pembuatan tahu. Sebanyak 30 percobaan hasil pengadukan didapati keseluruhan proses dinyatakan berhasil menggumpalkan sari kedelai pada kecepatan motor bervariasi sesuai kendali algoritma Fuzzy mamdani berdasarkan kondisi pH cuka dan suhu sari kedelai. Oleh karena itu penelitian ini menyimpulkan bahwa performa Algoritma Fuzzy mamdani dalam mengendaikan proses koagulasi pembuatan tahu melalui cara mengatur kecepatan pengadukan sebesar 100%. Temuan ini menjadi bukti penguat yang bisa dijadikan dasar bagi para peneliti bahwa algoritma fuzzy sekali lagi berhasil dijadikan rule pengendalian sebuah proses dengan hasil yang meyakinkan.
Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Identifikasi Jenis Tanaman Rimpang (Zingiberaceae) Rani Dwi Kartikasari; Mohammad Bhanu Setyawan; Fauzan Masykur; Adi Fajaryanto Cobantoro
MIKIR : Mathematics, Informatics, Knowledge And Information Research Vol. 1 No. 1 (2025): OKTOBER
Publisher : PT Mekar Research and Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Rhizomes (Zingiberaceae) are modified plant stems that grow horizontally beneath the soil surface and can produce shoots and new roots from their nodes. Rhizome plants (Zingiberaceae) are known as ginger or spice plants. This research article discusses the identification of rhizome plant species using Convolutional Neural Network (CNN) algorithm with VGG19 architecture, involving a total of 10 classes of data samples. The rhizome images underwent data preprocessing, resizing them from 500 x 500 to 200 x 200 pixels. During the model design phase, three different scenarios were tested, considering variations in dataset proportions, number of epochs, and batch sizes. The results of the three scenarios showed that the second scenario performed the best, achieving an accuracy of 90%, a loss of 0.285, precision of 93%, recall of 89%, and F1-Score of 91%. The first scenario obtained an accuracy of 88%, and the third scenario achieved an accuracy of 82%. However, when applying the model to test images and achieving the highest accuracy of 90% during training, the accuracy dropped to 40% when evaluated on 100 testing data. This drop in accuracy can be attributed to several factors, including noise in the dataset used and insufficient amount of training data, leading to the model being less effective in learning and recognizing data patterns.
CLASSIFICATION OF DURIAN LEAF IMAGES USING CNN (CONVOLUTIONAL NEURAL NETWORK) ALGORITHM Lely Mustikasari Mahardhika Fitriani; Yovi Litanianda; Adi Fajaryanto Cobantoro
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 2 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8576

Abstract

This research investigates the classification of durian leaf images using Convolutional Neural Network (CNN) algorithms, specifically focusing on the architectures AlexNet, InceptionNetV3, and MobileNet. The study begins with the collection of a dataset comprising 1604 images for training, 201 images for validation, and 201 images for testing. The dataset includes five classes of durian leaves: Bawor, Duri Hitam, Malica, Montong, and Musang King, chosen for their varied characteristics such as taste, texture, and aroma. Data preprocessing involved several steps to ensure the images were suitable for model training. These steps included data augmentation to increase variability, pixel normalization to standardize the images, and resizing to 150x150 pixels to match the input requirements of the CNN models. After preprocessing, the CNN models were implemented and trained using deep learning frameworks such as TensorFlow and PyTorch. Model performance was evaluated using a Confusion Matrix, which provided detailed insights into classification accuracy, precision, sensitivity, specificity, and F-score. The results indicated that InceptionNetV3 and AlexNet achieved near-perfect classification accuracy, with no misclassifications, demonstrating their robustness and precision in identifying durian leaf images. The training accuracy for both models rapidly approached 100% within the first few epochs and stabilized, while the loss values decreased sharply, indicating effective learning without overfitting. In contrast, MobileNet, while showing high accuracy and low loss during training, exhibited several misclassifications across all classes. The training accuracy of MobileNet also approached 100%, but the presence of misclassifications suggested that further tuning and improvements were necessary. Specifically, MobileNet's Confusion Matrix revealed errors in correctly identifying samples from each class, indicating potential areas for enhancement in the model's architecture or preprocessing techniques. In conclusion, InceptionNetV3 and AlexNet proved to be highly efficient and accurate architectures for classifying durian leaf images, making them suitable for practical applications. MobileNet, although performing well, requires further refinement to achieve the same level of accuracy and reliability. This study highlights the importance of selecting appropriate CNN architectures and the need for thorough preprocessing to optimize model performance in image classification tasks.
Co-Authors Aidha Putri, Evi Zulfa Angga Prasetyo Anugra, Fery Grahanda Arifin, Rizal Arifin, Rizal Ariyanti David April Riyanto Dwiyono Ariyadi Elok Putri Nimasari Evi Zulfa Aidha Putri Fauzan Masykur Fery Grahanda Anugra Hardiyan Oktavianto Ida Yeni Rahmawati Indah Puji Astuti Indah Puji Astuti Indah Puji Astuti Ismail Abdurrozaq Ismail Abdurrozaq Zulkarnain Jamilah Karaman Karisma Nanda Arditya Kelik Sussolaikah Khoiru Nur Fitri Lely Mustikasari Mahardhika Fitriani Miftahudin Agung Budi Wibowo Moh Bhanu Setyawan Moh. Bhanu Setyawan Mohammad Bhanu Setyawan Muhammad Reza Navila Uliya Sahidah Novia Anggraini Prasetyo, Angga Rani Dwi Kartikasari Siti Khoirul Bariyah Sony Dwiki Andika Susi Darihastining Tri Wahyono Viky Friandika Utamandani Yovi Litanianda Zulkarnain, Ismail Abdurrozzaq