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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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ANGKA LEMPENG TOTAL BAKPIA KACANG HIJAU DI KECAMATAN MOJOROTO, KEDIRI Widianingsih, Mastuti; Untoro, Meida Cahyo
Biosel Biology Science and Education Vol. 10 No. 1 (2021): BIOSEL (Biology Science and Education: Jurnal Penelitian Sains dan Pendidikan)
Publisher : INSTITUT AGAMA ISLAM NEGERI AMBON

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (439.609 KB) | DOI: 10.33477/bs.v10i1.1356

Abstract

Bakpia kacang hijau merupakan salah satu pangan yang mudah terkontaminasi mikroba. Tingkat kontaminasi dapat diketahui dengan menghitung jumlah mikroba pada sampel, salah satunya dengan ALT. Penelitian ini bertujuan untuk mengetahui jumlah mikroba pada bakpia kacang hijau dengan pemeriksaan ALT 1x104 koloni/gram sampel. Accidental sampling merupakan teknik sampling yang digunakan sehingga diperoleh sebanyak 30 sampel bakpia kacang hijau. Pemeriksaan ALT dilakukan dengan inkubasi secara aerob pada suhu 30°C selama 72 jam. Hasil penelitian menunjukkan terdapat 2 sampel bakpia kacang hijau yang tidak memenuhi standar nilai ALT 1x104 koloni/gram sampel. Hal tersebut dikarenakan proses pengolahan, penyimpanan, ataupun lingkungan eksternal (udara) yang menyebabkan terjadinya kontaminasi
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%
Transformasi Digital Layanan Posyandu: Pengembangan Sistem Informasi Berbasis Web di Desa Marga Agung Marbun, Rustian Afencius; Sinaga, Nydia Renli; Gunawan, Rayhan Fatih; Sianturi, Elsa Elisa Yohana; Revangga, Dwi Arthur; Siregar, Abu Bakar Siddiq; Laisya, Nashwa Putri; Yusuf, Muhammad; Untoro, Meida Cahyo; Praseptiawan, Mugi; Ashari, Ilham Firman; Widianingsih, Mastuti
PUBLIKASI PENGABDIAN KEPADA MASYARAKAT Vol 5 No 1 (2025)
Publisher : Fakultas Ekonomi dan Bisnis Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/padimas.v5i1.12206

Abstract

Posyandu memiliki peran strategis dalam pelayanan kesehatan masyarakat di tingkat desa, namun masih menghadapi berbagai kendala dalam pengelolaan data yang bersifat manual. Penelitian ini merancang dan mengimplementasikan sistem informasi Posyandu berbasis web untuk meningkatkan efisiensi pencatatan, keamanan data, serta kualitas layanan. Dengan pendekatan Modified Waterfall, pengembangan dilakukan melalui tahapan analisis kebutuhan, desain UI/UX, pengembangan perangkat lunak dengan teknologi NextJS dan PrismaDB, uji coba sistem, pelatihan kader, dan evaluasi efektivitas. Hasil pelaksanaan menunjukkan bahwa sistem ini mampu mempercepat pelaporan, meminimalkan kehilangan data, dan meningkatkan literasi digital kader. Sistem juga mendukung ekspor data untuk keperluan pelaporan desa serta penyajian grafik statistik kesehatan. Diharapkan sistem ini dapat direplikasi di wilayah lain sebagai bagian dari digitalisasi layanan dasar kesehatan desa.
Identifikasi Penyakit Pada Daun Kelapa Sawit Dengan Pendekatan CNN AlexNet Mandiri, Tobyanto Putra; Dharmawan, Benedictus Budhi; Ibn, Ferreyla Setara; Untoro, Meida Cahyo
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8456

Abstract

Oil palm is a plant that plays an important role in Indonesia's agricultural commodities. Cultivating oil palm is suitable for Indonesia due to its tropical climate, which greatly supports the growth of this plant. However, cultivating oil palm is not easy. The emergence of leaf diseases in oil palm can hinder growth, thereby affecting fruit production levels. This research aims to identify diseases on oil palm leaves using one of the methods of Deep Learning, namely the Convolutional Neural Network (CNN) method. This method was chosen because CNN leverages image-based datasets for classification and prediction, making it highly suitable for identifying diseases on oil palm leaves. The research begins with collecting a dataset of images of diseased oil palm leaves. The collected dataset will undergo pre-processing to enhance image quality, enabling more efficient processing by the model. The classification results will subsequently be evaluated to determine the accuracy level of the image processing performed by the model. By implementing Convolutional Neural Network, this research is expected to produce an effective and accurate system for identifying diseases on oil palm leaves, assisting farmers in cultivating oil palm, reducing losses, and ultimately increasing the productivity of oil palm plantations.
Naïve bayes classification for oil palm leaf disease based on color and texture features Kesuma, Alvin; Bangun, Natasya Ate Malem; Untoro, Meida Cahyo
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 3 (2025): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i3.305

Abstract

This study presents a comparison between standard Naïve Bayes classifier and its Genetic Algorithm-optimized variant for automated classification of oil palm leaf diseases. The system incorporates RGB color features alongside texture features extracted using the Gray Level Co-Occurrence Matrix. A dataset of of 225 JPG images of oil palm leaves, divided into training and testing sets in an 80:20 split is used. The methodology consisted of preprocessing, feature extraction, and classification. In the preprocessing phase, images were manually cropped, resized to 256 × 256 pixels, and background elements were removed. Feature extraction was then performed to obtain RGB color values and GLCM-based texture values, including contrast, correlation, energy, and homogeneity. Classification was conducted using two variants of the Naïve Bayes algorithm: one with default parameters and another optimized via GA for the Laplace smoothing hyperparameter. Model performance was assessed using a confusion matrix, with accuracy, precision, and recall serving as the primary evaluation metrics. Experimental results showed that both models achieved identical performance, with an accuracy of 51%, a precision of 52%, and a recall of 51%. These findings suggest that the Naïve Bayes classifier, even in its baseline form, demonstrates low discriminative performance for oil palm leaf disease detection, and when enhanced through GA-based optimization, it still provides only limited effectiveness. Therefore, this research highlights the need to pursue alternative methodologies, such as deep learning techniques or the adoption of more discriminative feature representations, aimed at improving both the accuracy and robustness of image-based disease detection in agriculture.
IoT-Based Hydroponic Plant Monitoring and Control System to Maintain Plant Fertility Untoro, Meida Cahyo; Hidayah, Fathan Rizki
INTEK: Jurnal Penelitian Vol 9 No 1 (2022): April 2022
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/intek.v9i1.3407

Abstract

Hydroponics is a method of cultivating plants by utilizing a small amount of land without using soil media. Hydroponic cultivation is still done conventionally in monitoring and controlling nutrients and pH of the air. Hydroponics is already with Internet of Things (IoT) technology in the cultivation process. The research aims to use IoT technology by developing control devices and monitoring hydroponic plants remotely, to make it easier for cultivators to control and monitor plant color, temperature, nutrients and the pH value of hydroponic plant water. Control and monitoring can be done through a smartphone application. The data from testing the condition of hydroponic plants obtained an average error of 1.8% for air temperature, 4.8% for water pH, 6.6% for plant color and 7% for water nutrients. Hydroponic plants with the TCS3200 sensor get a monitoring opportunity of 53.3%. Testing of tool control related to nutritional improvement has been carried out using the fuzzy Mamdani method with an increase in the probability of 88.75% for adding nutritional value and 0% for decreasing nutritional value. Tool control for improving the pH value of hydroponic plant water has been successfully carried out.
Sistem Monitoring dan Kontrol Budidaya Ikan Nila Berbasis IoT dengan Bioflok (Studi kasus: Kelompok Budidaya Ikan Sadewa Mandiri, Pringsewu) Ashari, Ilham Firman; Untoro, Meida Cahyo; Praseptiawan, Mugi; Afriansyah, Aidil; Nur'azmi, Eka
Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Vol 22, No 2 (2022): Suluah Bendang: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/sb.02760

Abstract

Kelompok pembudidaya ikan sadewa muda mandiri saat ini masih mengalami permasalahan dalam pembudidayaan ikan, hal ini dikarenakan control dan monitor mash dilakukan secara manual. Beberapa parameter yang terus dipantau oleh pembudidaya adalah PH, Kadar nutrisi air, dan suhu. Hal ini tentu saja tidak efektif dan memakan waktu. Oleh karena itu perlu system yang dapat melakukan monitoring terhadap parameter PH, kadar nutrisi air, dan suhu air, dan juga dapat melakukan control terhadap kualitas air. Hal ini dikarenakan kualitas air merupakan hal yang penting untuk budidaya ikan dengan teknologi bioflok. Dengan adanya system ini maka monitoring dan control dapat dilakukan dengan mudah lewat aplikasi mobile yang dapat terintegrasi dengan alat di luar, sehingga pembudidaya ikan tidak perlu datang dan melihat kondisi kolam secara berkala. Kegiatan pengabdian dilakukan dengan survei, persiapan pembuatan alat, pembuatan alat, integrasi alat, pengujain system, dan pelaksanaan kegiatan. Sistem ini sudah melakukan berbagai pengujian, seperti pengujian akurasi dan pengujian fungsional. Berdasarkan hasil pengujian akurasi,sensor suhu DS18B20 dan sensor DF Robot PH Meter V 1.1 memilik akurasi yang baik yaitu masing-masing 95,87% dan 98,28%. Sedangkan pada sensor Gravity TDS Meter V 1.0 masih belumcukup baik dimana persentase akurasi yang diperoleh adalah 93,44%.
Sistem Pantau dan Kontrol Budidaya Ikan Nila Berbasis IoT dengan Bioflok (Studi kasus: Kelompok Budidaya Ikan Sadewa Mandiri, Pringsewu) Ashari, Ilham Firman; Untoro, Meida Cahyo; Praseptiawan, Mugi; Afriansyah, Aidil
Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Vol 22, No 2 (2022): Suluah Bendang: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/sb.02680

Abstract

Kelompok pembudidaya ikan sadewa muda mandiri saat ini masih mengalami permasalahan dalam pembudidayaan ikan, hal ini dikarenakan control dan monitor mash dilakukan secara manual. Beberapa parameter yang terus dipantau oleh pembudidaya adalah PH, Kadar nutrisi air, dan suhu. Hal ini tentu saja tidak efektif dan memakan waktu. Oleh karena itu perlu system yang dapat melakukan monitoring terhadap parameter PH, kadar nutrisi air, dan suhu air, dan juga dapat melakukan control terhadap kualitas air. Hal ini dikarenakan kualitas air merupakan hal yang penting untuk budidaya ikan dengan teknologi bioflok. Dengan adanya system ini maka monitoring dan control dapat dilakukan dengan mudah lewat aplikasi mobile yang dapat terintegrasi dengan alat di luar, sehingga pembudidaya ikan tidak perlu datang dan melihat kondisi kolam secara berkala. Kegiatan pengabdian dilakukan dengan survei, persiapan pembuatan alat, pembuatan alat, integrasi alat, pengujain system, dan pelaksanaan kegiatan. Sistem ini sudah melakukan berbagai pengujian, seperti pengujian akurasi dan pengujian fungsional. Berdasarkan hasil pengujian akurasi, sensor suhu DS18B20 dan sensor DF Robot PH Meter V 1.1 memilik akurasi yang baik yaitu masing-masing 95,87% dan 98,28%. Sedangkan pada sensor Gravity TDS Meter V 1.0 masih belum cukup baik dimana persentase akurasi yang diperoleh adalah 93,44%.
Sentiment Analysis of Public Opinion on BAWASLU Using Random Forest and Particle Swarm Optimization Untoro, Meida Cahyo; Farhan, Muhammad
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.22234

Abstract

Purpose: Sentiment analysis, commonly referred to as opinion mining, involves the study of people's opinions, emotions, and attitudes toward various subjects. While the Random Forest algorithm is frequently employed in sentiment classification tasks, its integration with Particle Swarm Optimization (PSO) for feature selection remains relatively underexplored. This study investigates whether PSO-based feature selection can enhance the predictive performance of Random Forest by optimizing the selection of relevant textual features, ultimately leading to more accurate sentiment classification. Methods: The research adopts a structured text preprocessing approach that includes data cleansing, case folding, normalization, stop-word removal, and stemming to refine the input text. Term Frequency-Inverse Document Frequency (TF-IDF) is applied to extract features, followed by PSO-driven feature selection to refine the input set for the Random Forest classifier. The proposed model is evaluated using a Twitter sentiment dataset related to “Bawaslu”, with performance measured based on Out-of-Bag (OOB) error and accuracy metrics. Result: Empirical results demonstrate that incorporating PSO-based feature selection into the Random Forest model substantially lowers the OOB error to 20.42%, compared to 28.72% in the baseline Random Forest model. Furthermore, the optimized model achieves an accuracy of 78.35%, outperforming the standard approach. However, the introduction of PSO-based feature selection increases computational demands, indicating a trade-off between classification accuracy and processing efficiency. Novelty: This study introduces the novel integration of PSO-driven feature selection with Random Forest classification for sentiment analysis, addressing challenges in imbalanced text data. By optimizing feature selection through a metaheuristic approach, it enhances model accuracy and efficiency. The novelty lies in applying PSO to refine feature selection in text classification, offering new insights into improving machine learning models for imbalanced datasets. Future research could explore reducing computational overhead and investigating hybrid selection techniques to further enhance scalability and performance.
Enhancing Imbalanced Data Handling Using MWMOTE and K-Means Clustering Untoro, Meida Cahyo
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.69

Abstract

Machine learning and data mining, the quality of a dataset significantly influences model performance. One common issue is data imbalance, where one class in a dataset has significantly fewer samples than another. This imbalance can lead to biased models that favor the majority class, resulting in poor predictive performance for minority class instances. To address this issue, this study employs a resampling approach using the MWMOTE (Majority Weighted Minority Oversampling Technique) method, enhanced with K-Means Clustering. The MWMOTE algorithm generates synthetic samples for the minority class, while K-Means Clustering helps improve the distribution of generated samples by forming well-structured clusters. Experimental results on 10 different datasets demonstrate that the proposed MWMOTE + K-Means approach significantly improves classification performance. Compared to the baseline accuracy of 70%, the proposed method enhances precision by 10%, recall by 40%, and F-measure by 40%. However, the computational cost is slightly increased due to the additional clustering step required for synthetic data generation. Despite the increased computation time, the improvement in classification metrics suggests that integrating K-Means with MWMOTE is a promising technique for handling imbalanced data. Future research could explore optimizing the computational efficiency of this approach and comparing it with other oversampling techniques.
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