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All Journal Jurnal Ilmiah Informatika Komputer Teknika Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan CESS (Journal of Computer Engineering, System and Science) Jurnal CoreIT JURNAL KAJIAN TEKNIK ELEKTRO JTAM (Jurnal Teori dan Aplikasi Matematika) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi INTECOMS: Journal of Information Technology and Computer Science KACANEGARA Jurnal Pengabdian pada Masyarakat Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) IJID (International Journal on Informatics for Development) JURIKOM (Jurnal Riset Komputer) Jurnal Tekno Kompak TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Indonesian Journal of Electrical Engineering and Computer Science Bubungan Tinggi: Jurnal Pengabdian Masyarakat Jurnal Manajemen Informatika Jayakarta International Journal Software Engineering and Computer Science (IJSECS) Berdikari : Jurnal Pengabdian kepada Masyarakat ABDINE Jurnal Pengabdian Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science Technology and Informatics Insight Journal KAMI MENGABDI Journal of Data Science Theory and Application Journal of Digital Business and Management Prosiding Seminar Nasional Rekayasa dan Teknologi (TAU SNAR- TEK) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Edusight International Journal of Multidisciplinary Studies (EIJOMS) International Journal of Law Social Sciences and Management Computer Journal
Tundo, Tundo
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Penerapan IoT dengan Algoritma Fuzzy dan Mikrokontroler ESP32 dalam Monitoring Penyiraman Tundo; Sodik; Setiawan, Kiki; Aula, Raisah Fajri
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i3.977

Abstract

Smart farming has become a major focus in the development of modern agricultural technology. In this context, the Internet of Things (IoT) offers innovative solutions to increase productivity and efficiency in crop management. This research introduces an IoT-based plant watering monitoring system that uses a fuzzy algorithm and an ESP32 microcontroller. This system is designed to automatically regulate plant watering based on environmental conditions and plant needs. The ESP32 microcontroller acts as the brain of the system, collecting environmental data such as soil moisture, air temperature, and relative humidity. This data is analyzed using a fuzzy algorithm to determine plant watering needs in real-time. Based on the output of the fuzzy algorithm, the system automatically controls the water pump to water the plants according to the specified needs. The application of fuzzy algorithms allows the system to overcome uncertainty in plant watering decisions, especially in the face of complex variations in environmental conditions. With the adoption of IoT technology, farmers can monitor and control crop watering efficiently through web interfaces or mobile applications, even remotely. This research shows that the integration of IoT with fuzzy algorithms and ESP32 microcontrollers can be an effective solution in managing crop watering, increasing overall agricultural productivity, while reducing water and energy consumption.
Implementasi Algoritma Naive Bayes untuk Analisis Sentimen Terhadap Program Makan Siang Gratis Tundo; Rachmawati, Dea Noer
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i3.978

Abstract

The work program promised by the 2024 Presidential Candidate and Vice Presidential Candidate pair, namely, Prabowo Subianto and Gibran Rakabuming Raka, one of which is a free lunch program, this program is an effort to improve community welfare, but this has attracted public attention on social media, one the other is platform X. The public response to the free lunch program is the main focus. In this research, researchers analyzed sentiment related to the free lunch program using the Naive Bayes method using the Python programming language on Google Colab to analyze sentiment towards social media users. X. the results obtained from 920 tweets data, there are 167positive value tweets, 744tweets are negative. The evaluation results with the confusion matrix showed an accuracy of 86.95%, with precision 93%, recall 61%, and F1-Score 65%.From the results of this research it can be concluded that the majority of the public who commented on social media X gave a negative response to the free lunch program. These results can be used as material for government evaluation in determining effective and efficient policies for society.
Analisis Sentimen Tanggapan Pengguna Media Sosial X Terhadap Program Beasiswa KIP-Kuliah dengan Menggunakan Algoritma Support Vector Machine (SVM) Amelia, Ika; Sugiyono; Sarimole, Frencis Matheos; Tundo
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i3.990

Abstract

The KIP-Kuliah Scholarship is an Indonesian government program which aims to provide access to higher education for students from underprivileged families. This program has become a hot topic of discussion on social media, including social media. The object of research is comments on X's social media regarding the KIP-College Scholarship. Research methods include crawling data using google collabs, data preprocessing, Support Vector Machine model training, and model evaluation using RapidMiner. The research results show that the Support Vector Machine model is able to classify sentiment with an accuracy of 86.27%, but there is a bias towards negative sentiment. The majority of public responses are negative, often regarding misuse of scholarships. The suggestions given include collecting more balanced data, using dataset balancing techniques, implementing more complex models, and more in-depth evaluation to improve model performance. It is hoped that this research will provide input for the government in improving the KIP-College Scholarship distribution mechanism so that it is more targeted and reduces the potential for abuse.
Pengembangan Media Pembelajaran Berbasis Teknologi Augmented Reality (AR) dengan Algoritma Vuforia SDK pada Mata Pelajaran IPA Kelas VIII di Madrasah Al-Aqsha (MTS) Aryanti, Putri Gea; Rasiban; Sarimole, Frencis Matheos; Tundo
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i3.998

Abstract

This research aims to develop learning media in the form of an Android-based Augmented Reality application on human respiratory system material which can provide information about the introduction of respiratory system organs and their processes, and markerless images of the shapes of the organs which have been input into the Vuforia SDK library. This research develops an Augmented Reality application as an introduction to the organs of the respiratory system using several tools such as: MDLC, Unity, ARToolkit to better understand students about the respiratory system. The application of Augmented Reality in this research uses the Marker Based methods. The Augmented Reality application for recognizing the respiratory system in humans was tested using BlackBox with results of passing the system functional test 100% and usability testing results using a questionnaire. The learnability aspect was 4.47, efficiency 4.43, memorability 4.2, errors 4.5, and satisfaction 4.52, this application was tested in the "Good" category. Although there are problems with the quality of the camera and lighting, the test results show that this application is interesting and worthy of being developed again, so the implementation of Augmented Reality on MTS Al-Aqsa is the right step to support the method concept learning.
Klasterisasi Penggunaan Ban dengan Cost Per Kilometer Terendah pada PT. PL menggunakan Metode K-Means Syani, Muhammad; Tundo; Sugiyono; Wahyudi, Tri
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i3.1005

Abstract

Established in 1969, PT. Puninar has grown to become one of Indonesia's leading logistics companies. The company provides a broad range of logistics solutions through its subsidiaries, encompassing material management, manufacturing, warehousing, distribution, customs services, freight forwarding, and post-delivery operations. After fuel, tires represent the second-largest expenditure for the company. Proper tire management can reduce maintenance costs, making it a key cost-reduction strategy in the logistics sector. This study utilizes data mining techniques, specifically the K-means method, to analyze and classify tires based on travel distance and the lowest cost per kilometer, enabling monthly cost monitoring. PT. Puninar allocates approximately two billion rupiah per month for tire inventory due to its fleet of over seven hundred trucks. The company currently employs various tire brands, including Bridgestone, Doublecoin, Effiplus, Chengsang, and others, with varying levels of durability.
Klasifikasi Penjualan Produk Terlaris Pada Kedai Ira Dengan Menggunakan Algoritma Naïve Bayes Dan Algoritma K-Nearest Neighbor Purwasih, Intan; Setiawan, Kiki; Sarimole, Frencis Matheos; Tundo, Tundo
TEKNIKA Vol. 18 No. 2 (2024): Teknika Juli - Desember 2024
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.13236656

Abstract

Di dunia ritel dan teknologi saat ini, persaingan sangat kompetitif. Dengan Dengan pertumbuhan dan tersebarnya bisnis ritel di setiap wilayah, kebutuhan konsumen semakin meningkat, dan bisnis ritel berlomba-lomba untuk mengembangkan bisnis mereka dengan menggunakan teknologi saat ini. Data transaksi penjualan harian yang terus meningkat menyebabkan banyaknya penyimpanan. Toko Ira memiliki lebih dari 228 rekaman data transaksi penjualan dari tahun 2023 hingga 2024 yang belum digunakan. Data memerlukan banyak ruang penyimpanan. Selain itu, data tersebut belum digunakan dengan cara yang efektif. Berdasarkan masalah, tujuan penelitian ini adalah untuk menentukan barang mana yang paling laris dengan menggunakan data mining untuk mengklasifikasikan data transaksi penjualan. Studi kasus ini adalah studi kualitatif. Hasil penelitian ini menunjukkan bahwa algoritma K-Nearest Neighbors (KNN) dengan pembagian data 50:50 lebih efektif dalam memprediksi dan mengklasifikasikan penjualan produk laris dan tidak laris di toko kedai ira. Hasilnya menunjukkan bahwa algoritma Naive Bayes memiliki akurasi sebesar 89,91%, sedangkan algoritma K-Nearest Neighbors (KNN) dengan pembagian data 50:50 memiliki akurasi sebesar 89,91%.
Analisis Sentimen Ulasan Peserta Pelatihan Lpk Cipta Karya Intelektual Jakarta Timur Menggunakan Metode Naïve Bayes Maulana, Rizki; Tundo, Tundo; Sugiyono, Sugiyono; Wahyudi, Tri
INTECOMS: Journal of Information Technology and Computer Science Vol 7 No 5 (2024): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v7i5.11802

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen ulasan peserta pelatihan yang mengikuti kelas pelatihan berbasis daring di LPK Cipta Karya Intelektual, Jakarta Timur. Sistem manajemen pembelajaran LPK dan platform Karier.mu digunakan untuk memfasilitasi pembelajaran daring dan mengumpulkan ulasan peserta. Permasalahan utama yang diteliti adalah sentimen peserta terhadap program pelatihan yang diselenggarakan. Penelitian ini menggunakan metode analisis sentimen dengan algoritma Naïve Bayes, yang efektif untuk mengklasifikasikan sentimen dengan akurasi yang baik. Data yang dianalisis berasal dari ulasan peserta pada aplikasi Karier.mu. Hasil penelitian menunjukkan bahwa metode Naïve Bayes berhasil mengklasifikasikan ulasan menjadi kategori positif, netral, dan negatif dengan akurasi rata-rata sebesar 53,23% pada data testing dan 42,98% pada cross-validation. Sentimen positif dominan pada ulasan dengan rating bintang 4 dan 5, sementara sentimen negatif dominan pada rating bintang 1 dan 2. Rekomendasi praktis mencakup peningkatan kualitas materi pelatihan dan kompetensi instruktur. Penelitian ini memberikan wawasan berharga bagi LPK untuk meningkatkan kualitas program pelatihan dan menyusun strategi perbaikan yang lebih efektif. Implikasi dari penelitian ini adalah peningkatan kualitas layanan dan pengambilan keputusan yang lebih tepat berdasarkan analisis sentimen peserta.
Automatic Detection of Skin Diseases Using Convolutional Neural Network Algorithms Tundo; Prayogo, Fadillah Abi; Sugiyono
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.3021

Abstract

Skin diseases are a major health concern in Indone sia and they can seriously impact a patient’s quality of life. The problem is aggravated by humid tropical climate, limited access to healthcare facilities, and a lack of trained dermatology personnel. The cases in Indonesia are many, and the diagnosis and treatment of skin diseases are delayed, which makes the patient's condition worse. Based on data from the Ministry of Health (Kemenkes), the prevalence of skin disease in Indonesia is 0.62 cases per 10,000 population with the highest prevalence in Eastern Indonesia. Developing a Skin Disease Detection System Based on Convolutional Neural Network (CNN) algorithms. However, CNN algorithms are widely used in image recognition and classification, and can act as an automatic diagnostic system. This system has been developed to aid in diagnosis and improve patient access to dermatological care, especially for remote communities. Users can reach out for services at any time and any location, a practical solution for treating skin health problems. This study's results are anticipated to lower the diagnostic delays and improve the treatment outcomes while offering quick access to reliable dermatological service. This is a great effort on global level for any skin disease supporting to improve life of human lives from skin health issues.
Prediksi Produksi Sablon di Perusahaan Tomoinc dengan Perbandingan Metode Single Moving Average dan Single Exponential Smoothing Yacob, Galih Satria; Tundo; Mulyana, Dadang Iskandar; Lestari, Sri
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 1 (2025): JANUARI-MARET 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i1.3018

Abstract

A common problem faced by companies is predicting future production of goods based on previously recorded data. The company produces only according to orders, conducting production processes solely based on consumer demand. Any excess production is stored as stock to meet sudden consumer demands. These predictions significantly influence management decisions regarding the quantity of goods that must be prepared, considering factors like general business and economic conditions, competitors' actions, government policies, market trends, product life cycles, styles and fashions, changes in consumer demand, and technological innovations. This research aims to identify and analyze screen printing production predictions using the Moving Average and Exponential Smoothing methods. The more data used for comparison, the more accurate the prediction results. The research successfully developed a screen printing production prediction system, facilitating easier determination of future production quantities.
Analisis Sentimen Kepuasan Publik Terhadap Masa Kepemimpinan Shin Tae Yong Menggunakan Algoritma Naïve Bayes Nugraha, Pramudya; Rasiban; Sarimole, Frencis Matheos; Tundo
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 1 (2025): JANUARI-MARET 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i1.3020

Abstract

Shin Tae Yong is the coach of the Indonesian national team who has been a football player in South Korea and has coached the South Korean national team at the 2018 World Cup in Russia. Many people watch or pay attention to Shin Tae Yong's behavior and behavior when coaching the Indonesian national team. Shin Tae Yong has considerable worry with the Indonesian national team because of his strategy. However, there are several media that frame Shin Tae Yong's news differently so that differences in viewpoints and opinions on Shin Tae Yong are controversial, inviting many people to give their opinions. Therefore, people choose social media as a place to channel opinions. In this study, we will take tweets from X with search keywords for Shin Tae Yong and the Indonesian national team to process and classify the text using the sentiment analysis method. The text classification process is divided into two classes, namely positive sentiment classes and negative sentiment classes. The data used amounted to 2495 data that had been cleansed, which amounted to 2.348 Positive sentiment data and 147 data with negative sentiments so that they can be presented 98.94% positive and 60.00% negative, based on the classification of the Naïve Bayes algorithm model, using a split comparative data 0.8 :  0.2 With the value of k=3 for Shin Tae Yong's dataset, an accuracy value of 96.67%.
Co-Authors Abdus Salam, Abdus Ahmad Satria Rizqi Maula Akbar, Rasyan Akbar, Riolandi Akbar, Yuma Alief Prima Gani Amelia, Ika Arinal, Veri Arvianto, Ramdani Aryanti, Putri Gea Aula, Raisah Fajri Aulia Nur Septiani Azhar, Anisah Nurul Betty Yel, Mesra Betty Yel, Mesra Bobby Arvian James Dadang Iskandar Mulyana` Dalail Dalail Dalail, Dalail Devia, Elmi Dewantara, Rizki Dewanti, Elsa Mayorita Dharmawan, Tio Doni Kurniawan Doni Kurniawan Eldina, Ratih Enny Itje Sela Fakhrurrofi Fakhrurrofi Fakhrurrofi, Fakhrurrofi Faldo Satria Faridatun Nisa Gatra, Rahmadhan Hadi Gunawan, Hadi Haryati Heri Mahyuzar Heri Mahyuzar James, Bobby James, Bobby Arvian Januarsyah, Firly Joko Sutopo Julianda, Rindy Junaidi Junaidi Kasiono, Roy Kastum Kastum Kastum, Kastum Kevin Arya Josaphat Sitompul Khafid Nurohman Khana, Rajes Laras Sitoayu Lutfi Nugrahaini M. A. Burhanuddin Maharani, Delia Maharani, Shinta Aulia Mahardika, Fajar Mahyuzar, Heri Marliani, Tiara Marthy, Nicola Mohd Khanapi Abd Ghani Mubarak, Zulfikar Yusya Muhammad Nurdin Muhammad Syazidan Nabilah, Laila Nandang Sutisna Nisa, Faridatun Nizar, Amin Nugraha, Pramudya Nugrahaini, Lutfi Nugroho, Agung Yuliyanto Nugroho, Wisnu Dwi Nuradi, Fahmi Nurohman, Khafid Opi Irawansah, Opi Paidi, Imam Prayogo, Fadillah Abi Priyanto, Imansyah Purnasiwi, Rona Guines Purwasih, Intan Putri Wibowo, Salsabila Qolbi, Rofika Rachmat Hidayat Insani Rachmawati, Dea Noer Raden Dewa Saktia Purnama Raffiudin, Muhammad Raihanah, Syifa Ramadhan, Abhirama Huga Ramadhani, Devika Azahra Rasiban Ridho Akbar Rizki Maulana, Rizki Romadan, Diva Putra Saidah, Andi Saifullah, Shoffan Saktia Purnama, Raden Dewa Sarimole, Frencis Matheos Setiawan, Kiki Shofwatul ‘Uyun Sodik SOPAN ADRIANTO Sopan Adrianto Sri Lestari Sugeng Sugiono Sugiono Sugiyono Sugiyono Sugiyono Suropati, Untung Sutisna, Nandang Syani, Muhammad Tampubolon, Parlindungan Tasti, Andi Thalita Tiara Ratu Alifia Tresia, Eflin Tri Wahyudi Tundo Tundo Untung Suropati Wafiqi, Achmad Ulul Azmi Wagiman, Wagiman Waloeya, Farhan Adriansyah Wijonarko, Panji Yacob, Galih Satria