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All Journal TEKNIK INFORMATIKA TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Pixel : Jurnal Ilmiah Komputer Grafis Jurnal Informatika Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Teknologi dan Sistem Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika IT JOURNAL RESEARCH AND DEVELOPMENT JITK (Jurnal Ilmu Pengetahuan dan Komputer) Biosel: Biologi Science and Education Applied Information System and Management Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Telematika INTEK: Jurnal Penelitian Jurnal Manajemen Informasi Kesehatan Indonesia (JMIKI) Jurnal Pendidikan dan Konseling Ainet : Jurnal Informatika ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) Journal of Innovation Information Technology and Application (JINITA) Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi dan Komunikasi Innovation in Research of Informatics (INNOVATICS) Teknik: Jurnal Ilmu Teknik dan Informatika Parta: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Publikasi Hasil Pengabdian Kepada Masyarakat. Jurnal Karya Abdi Masyarakat Biner : Jurnal Ilmiah Informatika dan Komputer Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia Jurnal Informatika: Jurnal Pengembangan IT Jurnal Pengabdian Kepada Masyarakat Radisi Journal of Intelligent Systems and Information Technology Scientific Journal of Informatics Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Untoro, Meida Cahyo
Teknik Informatika, Institut Teknologi Sumatera
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Perancangan User Interface Sistem Informasi Luas Areal dan Produksi Perkebunan Rakyat Bidang Produksi Eri Yuni Nilasari; Meida Cahyo Untoro
Ainet : Jurnal Informatika Vol 4, No 1 (2022): Maret (2022)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/ainet.v4i1.7650

Abstract

Perancangan user interface system informasi untuk pendataan luas areal dan produksi pertanian pada fitur admin berbasis website di kantor Dinas Perkebunan Provinsi Lampung ini merupakan solusi bagi instansi karena dengan adanya sistem informasi ini dapat mempermudah pegawai/staf yang bertugas melakukan pendataan luas areal dan produksi perkebunan. Dikatakan mempermudah pekerjaan dalam pendataan karena sebelumnya proses pendataan masih dilakukan secara manual menggunakan excel. Dengan adanya system informasi luas areal dan produksi perkebunan  rakyat dapat membantu memperkecil terjadinya kesalahan dalam proses pendataan. Dalam tahap pembuatan desain system informasi luas areal dan produksi perkebunan rakyat menggunakan aplikasi Figma
Pelatihan Penerapan Sistem Integrasi Data Kependudukan Sederhana (SIDaKS) Di Kecamatan Kota Agung Timur Tanggamus Dani Al Mahkya; Fery Widhiatmoko; Dian Anggraini; Tirta Setiawan; Febri Dwi Irawati; Meida Cahyo Untoro
Jurnal Pengabdian kepada Masyarakat Radisi Vol 2 No 1 (2022): April
Publisher : Yayasan Kajian Riset dan Pengembangan RADISI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55266/pkmradisi.v2i1.68

Abstract

System-based data recording is indispensable in all fields, including government agencies. One of the important points in data recording is system integration. Data integration has the advantage of making the flow of organizational information better. One of the problems that arise in this Community Service activity partner is the unavailability of a population data integration system from each village/village to the sub-district. This activity aims to conduct training, mentoring and demonstration of the proposed integration system. The solution that will be offered is to conduct training and socialization related to population data management and integration. This training and socialization will use Ms. Excel and Google Sheet in the process. The method used in implementing community service activities is training and mentoring as well as demonstrations related to the Implementation of the Simple Population Data Integration System (SIDaKS) in Kota Agung Timur District, Tanggamus. The training was carried out in the East Kota Agung District hall, Tanggamus. Participants who attended the activity were representatives of each village/village in the Kota Agung Timur District. There are several steps taken to support the implementation of activities. The activity took place smoothly in accordance with the applicable health protocol. And it will be held on September 30, 2021 at the Kota Agung Timur , Tanggamus
Empowerment of Partner Schools Through the Development of the Mini Bank Application (m-MiniBank) at SMKN 7 Bandar Lampung Mugi Praseptiawan; ilham Firman Ashari; Samsu Bahri; Meida Cahyo Untoro; Arre Pangestu; Aidil Afriasnyah; Muhammad Habib Algifari
Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia Vol. 3 No. 9 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jppmi.v3i9.173

Abstract

SMKN 7 Bandar Lampung is an educational unit that manages a Mini Bank which is used for teaching factory practices in financial accounting practices, besides that it is also a school designated by the regional government of Lampung province which heads the Lampung regional public service agency (BLUD). The problems faced in the management of mini-banks are that the business processes are not running optimally, the transaction process is running manually, besides that the manager's capacity knowledge is also inadequate in managing it. The purpose of this community service activity is to develop the capacity and capabilities of partner schools through the development of the Mini Bank application. The empowerment method used is starting from identifying needs and problems, application design, implementation, testing, and finally evaluation. The result of this dedication is a mini bank application with testing carried out involving 10 respondents, the results of the UEQ (User Experience Questionnaire) test show the value of the efficiency aspect is 2.15, the accuracy aspect is 2.14, the clarity aspect is 2.44, the stimulation aspect that is 2.15 and the attractiveness aspect is 2.26 so that the scores on all aspects get an excellent score.
Hyperparameter Tuning Feature Selection with Genetic Algorithm and Gaussian Naïve Bayes for Diabetes Disease Prediction Ashari, Ilham Firman; Untoro, Meida Cahyo
Jurnal Telematika Vol. 17 No. 1 (2022)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v17i1.488

Abstract

Diabetes Mellitus is a disease that occurs due to disorders of carbohydrate, fat and protein metabolism associated with a lack of performance of insulin secretion. Diabetes is a degenerative disease that requires appropriate and serious treatment efforts. The effects lead to various complications of other serious diseases such as heart disease and stroke. Erectile dysfunction, kidney failure, nervous system damage, etc. Because there are so many impacts caused by diabetes, it is important to study this disease. The benefit of this study is to prevent the occurrence of severe complications and can help medical personnel in predicting this disease early and reduce the cost burden that arises due to this problem.  The purpose of this study is to determine the level of accuracy resulting from the use of feature selection with genetic algorithms and nave Bayes. In this study, predictions will be made using hyperparameter tuning with genetic algorithms and Naive Bayes optimization by performing feature selection. After conducting related research, it was found that the accuracy of 17 features using a genetic algorithm was better than modeling with 10 features. By using 17 features and hyperparameter tuning with genetic algorithm and naive Bayes modeling, the accuracy is 93.2%. By using 17 features without feature selection, the accuracy is 91.2%, there is an increase in accuracy of 1.5%.
Action Recommendation Model Development For Hydromon Application Using Deep Neural Network (DNN) Method Praseptiawan, Mugi; Athalla, Muhammad Nadhif; Untoro, Meida Cahyo
INNOVATICS: International Journal on Innovation in Research of Informatics Vol 5, No 2 (2023): September 2023
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v5i2.8422

Abstract

Controlling hydroponic plants, which is currently being carried out manually, can be said to be less effective because it still involves the hard work of farmers to continuously monitor the condition of the hydroponic plants. Therefore, the general objective of this research is to develop a model that can be used as a recommendation system for actions that farmers need to take based on hydroponic crop conditions. The model formed with this machine learning method will then be used in the Hydromon application which allows farmers to manage and monitor the condition of hydroponic plants and take action based on the recommendations given. This model was developed using a deep neural network algorithm consisting of five layers with the help of the TensorFlow framework. The results show that the model is accurate with an accuracy value of 96.47% on the test data to classify plant conditions so that it can be used in the Hydromon application.
Deteksi Malaria Berbasis Citra Mikroskop Menggunakan Metode Convolutional Neural Network Muttaqin, Muhammad; Untoro, Meida Cahyo; Algifari, Muhammad Habib; Faisal, Amir
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11488

Abstract

Malaria is a tropical disease that infects human red blood cells caused by infection with the plasmodium parasite. Plasmodium parasites spread to humans through female Anopheles mosquitoes and can reproduce in human blood cells. Malaria is a health problem that is at risk of causing other health problems such as anemia and even death. The current gold standard for malaria diagnosis is laboratory diagnosis by microscopic examination to find the malaria parasite through the blood cells of the patient. However, the diagnosis of malaria through microscopic observation of blood cells has the potential to take a long time, because the plasmodium parasite has a very small size. The malaria detection system using the Convolutional Neural Network (CNN) method is designed to detect malaria in human blood cells. CNN is a machine learning method designed to classify objects in an image. The system was built in three stages of development, namely the development of a CNN model for malaria detection, software development and hardware development. The hardware components used in the system include Raspberry pi, Raspberry Pi camera module, and LCD. The results of the malaria detection test using the CNN model gave an accuracy of 98.76% which was tested on blood cell images from a microscope
Evaluate of Random Undersampling Method and Majority Weighted Minority Oversampling Technique in Resolve Imabalanced Dataset Untoro, Meida Cahyo; Yusuf, Muhammad Asyroful Nur Maulana
IT Journal Research and Development Vol. 8 No. 1 (2023)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.12412

Abstract

Classification is a model for making predictions based on existing data. Imbalanced data leads to misclassification or modeling errors where the data is not relevant and results in poor classification modeling. A poor classification model is caused by imbalanced data in the classification label, and there is a need for data balancing as a solution to resolve this issue. The methods used to handle data imbalance are Random Undersampling and MWMOTE. The goal is to see the implementation of Random Undersampling and MWMOTE working well in addressing the imbalanced dataset and to know the performance and accuracy in modeling. The dataset used is an open source dataset from Kaggle consisting of Diabetes data, Bank Turnover data, Stroke data, and Credit Card data with various data ratios, with the goal of addressing the problem of imbalanced data. Model evaluation was performed using the confusion matrix and decision tree algorithm by looking at the precision, recall, f-measure, and accuracy values from the Random Undersampling and MWMOTE methods. Random Undersampling can address the problem of imbalanced data with a precision of 76.28%, recall of 76.74%, f-measure of 76.48%, and accuracy of 76.21%. MWMOTE can address the problem of imbalanced data with a precision of 86.04%, recall of 87.30%, f-measure of 86.66%, and accuracy of 86.61%. It can be concluded that the MWMOTE method is better than the Random Undersampling method because the average evaluation of the confusion matrix of the Random Undersampling method is smaller than the MWMOTE method.
SIBOX Smart Loker System with Dynamic Systems Development Method Untoro, Meida Cahyo; Praramadhana, Daffa; Suranta, Akmal Fauzan; Amrulloh, Iqbal; Praseptiawan, Mugi
Teknik: Jurnal Ilmu Teknik dan Informatika Vol 3 No 1 (2023): Mei: Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v3i1.302

Abstract

At this time logistics companies have a very important role in everyday life, especially in package delivery. The high level of online shopping is one of the reasons why the role of logistics is very important in our daily lives. Thus, seeing the common problems that occur in the conventional logistics delivery process, we came up with the idea to create an integrated smart locker to be one of the replacements for existing logistics outlets, we hope that with this application we can reduce the company's logistics expenses used in procuring logistics outlets and make it easier for couriers to work in the package delivery process. We created this application using a microservices architecture with the SDLC method used is Agile Dynamic System Development (DSDM). React JS framework as an interface and Express js and Laravel as an application that works behind the interface. The idea that we initiated we raised as a project within the company with the client from the company. In the end, the smart locker program made using the DSDM method has been completed and is ready to be implemented in the company and the client
Sistem Kontroling Dan Monitoring Hama Padi Berbasis Internet of Thing Di Kelompok Tani Bina Karya Pringsewu, Lampungb Untoro, Meida Cahyo; Praseptiawan, Mugi; Ashari, Ilham Firman; Yunira, Eka Nur'azmi; Hanifah, Raidah
Jurnal Karya Abdi Masyarakat Vol. 5 No. 3 (2021): Jurnal Karya Abdi Masyarakat
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (824.176 KB) | DOI: 10.22437/jkam.v5i3.17298

Abstract

Indonesia salah satu negara agraris dengan penghasil produk pertanian untuk keperluan pangan. Komoditas utama yang paling banyak dikembangkan dan ditanam pada sektor pertanian di Indonesia adalah padi. Lahan pertanian yang luas dan produksi padi yang melimpah menjadi unggulan. Lampung sendiri memiliki kabupaten penghasil taman pangan utama padi yang terdapat di kabupaten pringsewu dan sekaligus lumbung padi salah satunya Kelompok Tani Bina Karya. Kebutuhan pangan yang terus meningkat harus diimbangi dengan jumlah produksi padi yang cukup setiap tahunnya. Terlepas dari lahan dan produksi petani memiliki kendala pada masa tanam padi. Kendala yang terjadi mengakibatkan menurunnya hasil panen secara menyuruh atau malah gagal panen dikarenakan hama. Wereng coklat, belalang, burung bagian dari hama yang menyerang padi. Kelompok Tani Bina Karya melakukan pembasmian hama secara manual dengan menggunakan pestisida dan bertahan sementara. Penggunaan pestisida sebagai pembasmian hama secara kimiawi menimbulkan dampak pencemaran lingkungan, misalnya pencemaran air tanah, petani mengalami keracunan ketika melakukan penyemprotan pestisida. Untuk menyelesaikan permasalahan hama padi tim pengusul membuat teknologi tepat guna sebagai salah satu solusi alternatif dalam pengurangan serta kontroling dan monitoring lahan pertanian dari hama pengganggu dan kondisi lahan. Sistem Kontroling dan Monitoring Hama Padi Berbasis Internet of Thing di Kelompok Tani Bina Karya Pringsewu, Lampung.
Prediksi Penyakit Daun Pisang Menggunakan Metode LSTM (Long Short-Term Memory) Ba’its, Alfian Kafilah; Bagaskara, Radhinka; Setiawan, Andika; Yulita, Winda; Harmiansyah, Harmiansyah; Listiani, Amalia; Untoro, Meida Cahyo; Drantantiyas, Nike Dwi Grevika; Faisal, Amir; Anggraini, Leslie; Febrianto, Andre; Aprilianda, Mohamad Meazza; Fitrawan, Mhd. Kadar
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 10 No. 1 : Tahun 2025
Publisher : LPPM UNIKA Santo Thomas

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

Abstract

Dalam sektor pertanian, tanaman yang memiliki peran signifikan dalam skala global adalah pisang, yaitu buah yang mudah didapatkan, dapat tumbuh dimana saja, memiliki gizi yang tinggi, serta memiliki nilai ekonomi & budaya yang tinggi. Pisang mempunyai kontribusi yang signifikan terhadap pendapatan nasional Indonesia, terutama di Provinsi Lampung sebagai penghasil pisang nasional terbesar. Tetapi, proses produksi pisang seringkali mengalami kendala, salah satunya karena faktor serangan penyakit Black Sigatoka. Penyakit tersebut memberikan kerugian pada tanaman pisang, seperti daun yang meranggas, panen tertunda, bakal buah rontok, dan kualitas buah yang rendah, dan dapat menyebar melalui aliran udara atau percikan air hujan. Tingkat keparahan penyakit Black Sigatoka perlu diprediksi agar penyakit tersebut dapat dikontrol dan dapat dicegah sedini mungkin. Model yang digunakan untuk memprediksi permasalahan ini dalam jangka panjang adalah model Long Short-Term Memory (LSTM), salah satu jenis dari arsitektur Recurrent Neural Network (RNN), yang mempunyai kinerja yang baik dan mempunyai model yang prediktif. Aplikasi LSTM diterapkan terhadap dataset pohon pisang yang terdampak penyakit Black Sigatoka. Hasil dari model LSTM dalam melakukan prediksi penyakit Black Sigatoka menghasilkan model dengan nilai error yang kecil, dengan nilai MAE dan MAPE masing-masing sebesar 0.084 dan 5.7%
Co-Authors Afriansyah, Aidil Ahmad Agung Zefi Syahputra Aidil Afriasnyah Algifari, Muhammad Habib Amrulloh, Iqbal Anastasia Puteri Dewi Andika Setiawan Andika Setiawan, Andika Andini, Maria Anggraini, Leslie Annisa Dwi Atika Anugerah Perdana Aprilia Purwanto Aprilianda, Mohamad Meazza Arre Pangestu Athalla, Muhammad Nadhif Bagaskara, Radhinka Bangun, Natasya Ate Malem Ba’its, Alfian Kafilah Buliali, Joko Lianto Dani Al Mahkya Desi Budiarti Dharmawan, Benedictus Budhi Dian Anggraini Drantantiyas, Nike Dwi Grevika Eka Nur'azmi Yunira Eko Dwi Nugroho Eri Yuni Nilasari Faisal, Amir Faza Nur Fuadina Febrianto, Andre Feri Fahrianto Fery Widhiatmoko Fitrawan, Mhd. Kadar Gunawan, Rayhan Fatih Harmiansyah Hidayah, Fathan Rizki Ibn, Ferreyla Setara Ilham Firman Ashari Irawati, Febri Dwi Jerhi Wahyu Fernanda Kesuma, Alvin Kurniawansyah, Apri Laisya, Nashwa Putri Leo Viranda Millennium Leonard Rizta Listiani, Amalia M. Syamsuddin Wisnubroto Mahdia Nisrina Maharani M Mandiri, Tobyanto Putra Marbun, Rustian Afencius Maria Oktarise Natania Gultom Mastuti Widianingsih, Mastuti Muhammad Adam Aslamsyah Muhammad Affandi Muhammad Alfarizi Tazkia Muhammad Farhan Muhammad Muttaqin Muhammad Nadhif Athalla Muhammad Yusuf Muhammad Zulfarhan Najie, Muhammad Nasrulloh, M. Anas Nazla Andintya Wijaya Nestiawan Ferdiyanto Nur'azmi, Eka Nurul Fajrin Ariyani Oktaviana Rinda Sari Perdana, Agung Mahadi Putra Prabandari, Pungki Resti Praramadhana, Daffa Praseptiawan, Mugi Pungki Resti Prabandari Raidah Hanifah Raidah Hanifah Retnosari, Hesti Revangga, Dwi Arthur Riyanarto Sarno Samsu Bahri Sianturi, Elsa Elisa Yohana Sidabutar, Ribka Julyasih Sinaga, Nydia Renli Siregar, Abu Bakar Siddiq Sofian, Ahmad Alif Sophia Nouriska Suranta, Akmal Fauzan Tirta Setiawan Verdiana, Miranti Winda Yulita Wisnubroto, M. Syamsuddin Yulita, Winda Yunira, Eka Nur'azmi Yusuf, Muhammad Asyroful Nur Maulana