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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Indonesian Journal on Software Engineering (IJSE) JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Multidisiplin Moneter : Jurnal Keuangan dan Perbankan
Saputra, Filmada Ocky
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Public Health Social Sciences Other

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Pengelolaan Sampah Rumah Tangga Berbasis Aplikasi Pada Seluruh Bank Sampah Di Kecamatan Semarang Barat Saputra, Filmada Ocky; Ingsih, Kusni; Kartikadarma, Etika; Isthika, Wikan; Johary, Lakui; Sakti, Maulana Bima
Jurnal Pengabdian Multidisiplin Vol. 3 No. 2 (2023): Jurnal Pengabdian Multidisiplin
Publisher : Kuras Institute & Scidac Plus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51214/japamul.v3i2.632

Abstract

Data jumlah produksi sampah rumah tangga di Kota Semarang mencapai 1.110 ton perhari. Secara keseluruhan sampah tersebut dibuang ke tempat pembuangan akhir (TPA) setiap harinya. Dinas Lingkungan Hidup (DLH) bersama dengan pemerintah Kota Semarang mencanangkan program 1.000 bank sampah terdapat dikota Semarang dengan kondisi saat ini terdapat 229 yang telah beroperasi. Harapan dengan semakin banyaknya bank sampah maka sampah yang masuk ke pembuangan sementara dan TPA dapat berkurang karena terjadi proses pilah pilih dan olah sampah sebelum dibuang pada TPA. Mitra pengusul dalam kegiatan PKM ini merupakan bank sampah di kawasan kecamatan Semarang Barat, yang memiliki 16 kelurahan dimana pada setiap kelurahan setidaknya memiliki 1 bank sampah yang aktif. Namun kondisi pada pengelolaan bank sampah pada setiap kelurahan masih berbeda, berdasarkan hasil survey tim pengusul ditemukan beberapa bank sampah yang dikelola melalui LPMK Kelurahan dan terdapat pula bank sampah yang dikelola secara individu. Hal ini menjadikan kontrol yang susah pada tingkat kecamatan untuk mengetahui pengelolaan sampah di kecamatan Semarang Barat melalui bank sampah ini. Permasalahan lain yang terjadi adalah pengelola bank sampah belum dapat menjual produk olahan sampah dengan efektif baik dari segi media penjualan dan perhitungan biaya produksi. Tim mengusulkan kegiatan PKM dengan memberikan solusi sistem pengelolaan sampah berbasis aplikasi digital dan pendampingan dalam merancang kebuatuhan biaya produksi dan sumber daya manusia. Dalam pelaksanaan tim pengusul akan mengunakan metode community based research (CBR), metode ini digunakan untuk dapat secara tepat mengatasi permasalahan riil yang terjadi dimasyarakat, sehingga tujuan yang diinginkan dapat tercapat sesuai permasalahan yang dialami masyakarat. Dalam pelaksanaan respon positif dari peserta dengan secara aktif berdiskusi, melakukan pemasangan aplikasi, pendaftaran akun, serta mencoba fitur, sehingga data timbulan sampah yang disetorkan dan diolah oleh bank sampah dapat terintegrasi se kecamatan Semarang Barat.
Prediksi Banjir Berdasarkan Indeks Curah Hujan Menggunakan Deep Neural Network (DNN) Fafaza, Safira Alya; Rohman, Muhammad Syaifur; Pramunendar, Ricardus Anggi; Sri Winarsih, Nurul Anisa; Saraswati, Galuh Wilujeng; Saputra, Filmada Ocky; Ratmana, Danny Oka; Shidik, Guruh Fajar
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7098

Abstract

Floods are natural disasters that often occur and are among the most destructive because they have significant economic and social impacts. Accurate flood predictions are essential to manage risk and organize emergency response planning effectively. This research uses Deep Neural Network (DNN) to build a flood forecasting model that relies on rainfall index indicators and captures complex and ever-changing patterns obtained from rainfall index data. Using historical information from flood disaster events in Kerala, India, an analysis was conducted to assess the impact of various factors, particularly in learning rate and optimizer type, on model performance. The experimental results show that the type of optimizer is a crucial factor in determining the model's effectiveness, as shown in the ANOVA statistics with a P-value of 0.008493, much lower than the general threshold of 0.05. This is because this type of optimizer can significantly improve prediction accuracy. With the Adam optimizer type, the learning rate range is between 0.1 and 0.4, showing an accuracy level of up to 100%. However, the choice of learning rate does not significantly impact, indicating that the main emphasis on parameter adjustment should be determined accurately. Therefore, by carrying out appropriate parameter adjustments and thorough validation to find the optimal configuration that can increase accuracy in predicting flood disasters based on rainfall indices, the DNN model has the potential to become a tool that can assist in flood risk planning and management.
Perbandingan Efektivitas Nave Bayes dan SVM dalam Menganalisis Sentimen Kebencanaan di Youtube Azzahra, Tarissa Aura; Winarsih, Nurul Anisa Sri; Saraswati, Galuh Wilujeng; Saputra, Filmada Ocky; Rohman, Muhammad Syaifur; Ratmana, Danny Oka; Pramunendar, Ricardus Anggi; Shidik, Guruh Fajar
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7186

Abstract

Advancements in the field of Natural Language Processing (NLP) have opened significant opportunities in sentiment analysis, particularly in the context of disaster response. In today's digital era, YouTube has emerged as a primary source for the public to acquire information regarding critical events. This study explores and compares two dominant sentiment analysis techniques, namely Naive Bayes and Support Vector Machine (SVM). It utilizes YouTube comment data related to natural disasters to test the effectiveness of these algorithms in identifying and classifying public sentiment as neutral, positive, or negative. The process involves collecting comment data, pre-processing the data, and applying Term-Frequency-Inverse Document Frequency (TF-IDF) weighting to prepare the data for analysis. Subsequently, the performance of both models is evaluated based on metrics such as accuracy, precision, recall, and F1 score. The results indicate that while both algorithms have their strengths and weaknesses, SVM tends to show better performance in sentiment classification, especially in terms of accuracy and precision, with an accuracy result of 92% and precision of 89% for negative predictions and 94% for positive predictions. On the other hand, Naive Bayes only achieved an accuracy of 79% and a precision of 91% for negative predictions and 73% for positive predictions. This study provides significant insights into the application of machine learning algorithms in sentiment analysis.
Forecasting Air Quality Indeks Using Long Short Term Memory Ramadhani, Irfan Wahyu; Saputra, Filmada Ocky; Pramunendar, Ricardus Anggi; Saraswati, Galuh Wilujeng; Winarsih, Nurul Anisa Sri; Rohman, Muhammad Syaifur; Ratmana, Danny Oka; Shidik, Guruh Fajar
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7402

Abstract

Exercise offers significant physical and mental health benefits. However, undetected air pollution can have a negative impact on individual health, especially lung health when doing physical activity in crowded sports venues. This study addresses the need for accurate air quality predictions in such environments. Using the Long Short-Term Memory (LSTM) method or what is known as high performance time series prediction, this research focuses on forecasting the Air Quality Index (AQI) around crowded sports venues and its supporting parameters such as ozone gas, carbon dioxide, etc. -others as internal factors, without involving external factors causing the increase in AQI. Preprocessing of the data involves removing zero values "‹"‹and calculating correlations with AQI and the final step performs calculations with the LSTM model. The LSTM model which adds tuning parameters, namely with epoch 100, learning rate with a value of 0.001, and batch size with a value of 64, consistently shows a reduction in losses. The best results from the AQI, PM2.5, and PM10 features based on performance are MSE with the smallest value of 6.045, RMSE with the smallest value of 4.283, and MAE with a value of 2.757.
Pelatihan Logika Dasar Pemrograman menggunakan Code.org pada SMA Negeri 1 Bergas Winarsih, Nurul Anisa Sri; Pramunendar, Ricardus Anggi; Saputra, Filmada Ocky; Rohman, Muhammad Syaifur; Ratmana, Danny Oka; Hamid, Maulana As’an; Kartika, Gita
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 1 (2025): JANUARI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i1.2779

Abstract

Program "Pelatihan Logika Dasar Pemrograman Menggunakan Situs Web Code.org" bertujuan untuk menyediakan platform yang komprehensif dan mudah diakses bagi individu yang ingin meningkatkan keterampilan dasar logika pemrograman mereka. Program ini menggunakan situs web Code.org, sumber daya online yang ramah pengguna, untuk menyampaikan modul pelatihan yang menarik dan interaktif. Peserta akan dibimbing melalui konsep dasar logika pemrograman, membentuk pemahaman yang kuat tentang prinsip-prinsip kunci yang menjadi dasar berbagai bahasa pemrograman. Integrasi platform Code.org memastikan pengalaman belajar yang intuitif, menjadikannya cocok untuk pemula sambil menawarkan wawasan berharga bagi mereka yang memiliki latar belakang pemrograman tertentu. Pendekatan terstruktur dan latihan praktis program memberdayakan peserta untuk mengembangkan keterampilan dasar pemecahan masalah dan berpikir algoritmik, yang pada akhirnya mempersiapkan mereka untuk upaya pemrograman yang lebih canggih.
PENGEMBANGAN ARSITEKTUR REST API UNTUK INTEGRASI DATA REAL-TIME PADA WEBSITE PEMANTAUAN KUALITAS UDARA LAHAN PERTANIAN Bramantyo, Satrio Bisma; Dewi, Ika Novita; Reza, Ivan Muhammad; Saputra, Filmada Ocky; Hasibuan, Zainal Arifin
Transmisi: Jurnal Ilmiah Teknik Elektro Vol 27, No 1 Januari (2025): TRANSMISI: Jurnal Ilmiah Teknik Elektro
Publisher : Departemen Teknik Elektro, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/transmisi.27.1.49-56

Abstract

Transformasi teknologi dalam sektor pertanian semakin berkembang dengan adopsi perangkat Internet of Things (IoT) yang mampu menghasilkan data real-time terkait kualitas udara, seperti konsentrasi CO2, NO2, dan CH4. Meskipun data yang dihasilkan sangat berharga, namun tantangan besar masih dihadapi dalam hal penyimpanan dan manajemen data yang terfragmentasi. Penelitian ini bertujuan untuk mengembangkan arsitektur REST API yang mampu mengintegrasikan perangkat IoT, basis data, dan antarmuka pengguna dalam bentuk website pemantauan kualitas udara lahan pertanian. REST API digunakan untuk memvalidasi, memproses, dan memformat data yang kemudian dikirimkan ke pusat database melalui protokol HTTP standar (GET, POST). Selain itu, protokol WebSocket diterapkan untuk memastikan komunikasi dua arah yang memungkinkan transmisi data secara real-time antara perangkat IoT dan antarmuka pengguna. Hasil pengujian menunjukkan bahwa arsitektur ini mampu memberikan informasi yang akurat dan cepat kepada petani, mendukung pengambilan keputusan yang lebih baik dalam pengelolaan lahan, serta berkontribusi pada pengurangan emisi gas rumah kaca.
Talk to Calm, Aplikasi Pendukung Kesehatan Mental untuk Generasi Masyarakat Cerdas Manaf, Humam Wajdi; Tanjung, Mohamad Aprilian; ‘Aini, Fariida Qurrota; Finoe, Albireo Musyaffa; Saputra, Filmada Ocky; Paramita, Cinantya
Indonesian Journal on Software Engineering Vol 11, No 1 (2025): IJSE 2025
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijse.v11i1.25876

Abstract

Kesehatan mental menjadi isu penting di Indonesia, terutama pada kalangan remaja, dengan data menunjukkan bahwa 1 dari 20 juta remaja mengalami gangguan mental. Sayangnya, akses terhadap layanan konseling profesional masih sangat terbatas, diperparah dengan tingginya stigma masyarakat terhadap isu kesehatan mental. Kondisi ini memunculkan fenomena self-diagnose yang berisiko memperburuk kondisi psikologis individu. Menanggapi permasalahan ini, kami mengembangkan TaCa (Talk to Calm), sebuah aplikasi mobile berbasis digital untuk mendukung kesehatan mental masyarakat. Aplikasi ini menghadirkan berbagai fitur seperti Chatbot, Kalkulator Kesehatan Mental, Konsultasi Profesional, Mood Tracker, Podcast, dan Artikel Edukasi. Pengembangan aplikasi menggunakan pendekatan metodologi Agile, yang memungkinkan proses adaptif dan iteratif sesuai dengan kebutuhan pengguna. TaCa diharapkan dapat meningkatkan pemahaman masyarakat mengenai kesehatan mental, memperluas akses terhadap layanan konseling profesional yang berkualitas, serta mengurangi stigma sosial yang selama ini melekat. Inovasi ini juga mendukung pembangunan smart city, khususnya dalam dimensi smart society, dengan memanfaatkan teknologi untuk menjawab permasalahan sosial yang krusial di Indonesia.
DESIGN OF IOT AND ONION AGRICULTURE DATABASE USING BPR LIFE CYCLE Thifaal, Nisrina Salwa; Alzami, Farrikh; Steven, Alvin; Yusianto, Rindra; Saputra, Filmada Ocky; Sartika, Mila; Andono, Pulung Nurtantio; Wahyudi, Firman
Moneter: Jurnal Keuangan dan Perbankan Vol. 11 No. 1 (2023): APRIL
Publisher : Universitas Ibn Khladun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (632.824 KB) | DOI: 10.32832/moneter.v11i1.54

Abstract

One of the food commodities produced by the agricultural sector with high economic value is red onion. As the population of Indonesia increases, the need for red oniom has also increased. The level of red onion production from year to year is also increasing. Especially the central Java area as the largest red onion producing center in 2021. Therefore, the amount of red onion production needs to be maintained and increased by monitoring overall land conditions. Such as weather conditions, air, temperature, and humidity. A sensor to detect these factors is already available but there is no database to accommodate the data from the sensor. The purpose of this research is to produce a Business Process Model and Notation (BPMN) of red onion surveillance system on Internet of Things (IoT) based farmland. The stages carried out are by collecting data related to the research and analyzing business processes using the Business Process Reengineering Life Cycle (BPR) method. This method improves business processes to become more efficient and renewable. This research produces a database design to accommodate incoming data from Internet of Things sensors. Things (IoT) on red onion farming.
Implementation Of Extreme Gradient Boosting Algorithm For Predicting The Red Onion Prices Saputri, Pungky Nabella; Alzami, Farrikh; Saputra, Filmada Ocky; Andono, Pulung Nurtantio; Megantara, Rama Aria; Handoko, L Budi; Umam, Chaerul; Wahyudi, Firman
Moneter: Jurnal Keuangan dan Perbankan Vol. 11 No. 1 (2023): APRIL
Publisher : Universitas Ibn Khladun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (656.456 KB) | DOI: 10.32832/moneter.v11i1.55

Abstract

Red Onion or the Latin name Allium Cepa is included in the group of vegetable plants that are needed by the public for food needs. Red Onions are one of the seasonal crops so their availability can change in the market which causes price instability due to a lack of supply of production by several factors: 1) not yet it's harvest time, 2) crop attacked disease pests and fungi, and 3) weather factor. Therefore, a study is needed to predict red onion prices, so that it can be used as information for the government to stabilize red onion prices. The method used in this study is CRISP-DM and the Extreme Gradient Boosting algorithm to predict the price of red onions by taking data samples from Tegal and Pati Cities. The results of this study are that the Extreme Gradient Boosting algorithm is able to produce Tegal District Root Mean Square Error (RMSE) values of 5107.97% and Mean Absolute Percentage Error (MAPE) values of 0.17%. For prediction results with Pati Regency data samples, it produces a Root Mean Square Error (RMSE) value of 6049.74% and a Mean Absolute Percentage Error (MAPE) of 0.17%.
Co-Authors Alzami, Farrikh Azzahra, Tarissa Aura Bramantyo, Satrio Bisma Chaerul Umam Etika Kartikadarma Fafaza, Safira Alya Finoe, Albireo Musyaffa Firman Wahyudi, Firman Guruh Fajar Shidik Hamid, Maulana As’an Ika Novita Dewi Johary, Lakui Kartika, Gita Kusni Ingsih L. Budi Handoko Manaf, Humam Wajdi Megantara, Rama Aria Mila Sartika, Mila Paramita, Cinantya Pulung Nurtantio Andono Ramadhani, Irfan Wahyu Ratmana, Danny Oka Reza, Ivan Muhammad Ricardus Anggi Pramunendar Rohman, Muhammad Syaifur Sakti, Maulana Bima Saputri, Pungky Nabella Saraswati, Galuh Wilujeng Steven, Alvin Tanjung, Mohamad Aprilian Thifaal, Nisrina Salwa Wikan Isthika, Wikan Winarsih, Nurul Anisa Sri Yusianto Rindra Zainal Arifin Hasibuan ‘Aini, Fariida Qurrota