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All Journal Jurnal Ilmu Komputer JAIS (Journal of Applied Intelligent System) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Sistem Informasi dan Aplikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) KOMPUTIKA - Jurnal Sistem Komputer Progresif: Jurnal Ilmiah Komputer Jurnal Manajemen Informatika dan Sistem Informasi Budimas : Jurnal Pengabdian Masyarakat Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika: Jurnal Pengembangan IT Science Technology and Management Journal (STMJ) Inovasi Sosial: Jurnal Pengabdian Masyarakat
Saraswati, Galuh Wilujeng
Universitas Dian Nuswantoro
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Metode MICE Support Vector Machine (MICE-SVM) untuk Klasifikasi Performance Mahasiswa Merdeka Belajar Kampus Merdeka Angga Apriano Hermawan; Galuh Wilujeng Saraswati; Etika Kartikadarma
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6821

Abstract

The Ministry of Education and Culture established a Merdeka Belajar Kampus Merdeka (MBKM) program with the aim of improving the competency of student graduates, both soft skills and hard skills, so that they are better prepared and relevant to the needs of the times, preparing graduates as future leaders of the nation who are superior and have personality. However, the MBKM program is not always effective in improving the quality of a student because there are still several shortcomings. It is also felt that some students have not received maximum results when participating in the MBKM program. In fact, not all programs offered by MBKM partners receive an assessment in the form of soft skills scores. The aim of this research is to classify whether the MBKM program influences the performance of MBKM program students by applying the Multivariate Imputation by Chained Equation (MICE) method to overcome missing values in the classification of MBKM student performance at the Faculty of Computer Science, Dian Nuswantoro University. The qualification of MBKM student performance is very important because we need to know whether the program is deemed effective or not to be continued in the future. In this study, researchers used a dataset originating from the MBKM report from students at the Faculty of Computer Science, Dian Nuswantoro University. Researchers obtained data by collecting data from MBKM student certificates and reporting the results. The data taken was 277 pieces for training and 69 pieces for testing. Next, the researchers used the Support Vector Machine (SVM) algorithm for the classification process. The research results show that the performance of the Support Vector Machine (SVM) algorithm model with MICE missing value handling has better accuracy results, with an accuracy value of 98.07% compared to using the Mean Imputation method, which only obtains an accuracy of 97.34%.
PENGEMBANGAN APLIKASI SISTEM INFORMASI RESIK BECIK (SIKECIK) BERBASIS WEB PADA RUMAH SAMPAH RESIK BECIK KELURAHAN KROBOKAN SEMARANG Meilani Dwi Permatasari; Dianna Yanuaresta; Rino Agung; Etika Kartikadarma; Lakui Johary; Galuh Wilujeng Saraswati; Filmada Ocky Saputra
BUDIMAS : JURNAL PENGABDIAN MASYARAKAT Vol 4, No 2 (2022): BUDIMAS : VOL. 04 NO. 02, 2022
Publisher : LPPM ITB AAS Indonesia Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/budimas.v4i2.6739

Abstract

Semarang merupakan salah satu kota penghasil sampah terbesar di Indonesia. Sekitar 1.270 ton sampah per hari dan sekitar 900 ton di antaranya dikirim ke Tempat Pembuangan Akhir (TPA) setiap harinya dan hanya sebagian kecil dari sampah yang di daur ulang. Rumah Sampah Resik Becik merupakan bank sampah yang menampung sampah dari nasabah berupa kertas karton, plastik, logam, kaca, hingga cangkang telur. Rumah Sampah Resik Becik merupakan singkatan dari ’Gerakan Bersih Kreatif Bersama Ciptakan Kemakmuran’ dan rumah sampah didirikan karena jumlah sampah di kota Semarang semakin mengkhawatirkan yang setiap tahunnya mengalami peningkatan hingga 10%. Proses bisnis di rumah sampah resik becik masih dilakukan secara manual dan para pengurus masih kesulitan dalam mendata nasabah, sampah, dan saldo secara langsung. Oleh karena itu, diperlukan adanya digitalisasi manajemen sistem pada Rumah Sampah Resik Becik melalui sistem SIKECIK yang berbasis website dan mobile application.
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.
PENGEMBANGAN SISTEM INFORMASI MANAJEMEN PENGELOLAAN SAMPAH MENGGUNAKAN METODE EXTREME PROGRAMMING Galuh Wilujeng Saraswati; Malik Aziz Ali
Jurnal Manajemen Informatika dan Sistem Informasi Vol. 7 No. 2 (2024): MISI EDISI JUNI 2024
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/misi.v7i2.1132

Abstract

Sampah merupakan tantangan di Kota Semarang, terutama dengan produksi sampah yang signifikan dan pertumbuhan jumlah penduduk yang ikut meningkatkan jumlah sampah.  Untuk mengatasi masalah ini, setiap Kelurahan membentuk Bank Sampah dengn kader ibu-ibu PKK sebagai solusi untuk mengurangi produksi sampah. Setiap triwulan, Kelurahan Gisikdrono, perlu melaporkan data pengelolaan sampah yang dikelolah oleh masing-masing bank ke Dinas Lingkungan Hidup (DLH). Namun, dikarenakan sistem administrasi pencatatan yang dilakukan setiap kader masih manual dan tidak terdokumentasi dengan baik membuat proses pelaporan terkendala serta kesulitan dalam manajemen data pengelolaan sampah. Pengembangan sistem informasi pengelolaan sampah secara digital dapat mempermudah manajemen pendataan sampah dan pelaporan pada DLH. Tujuan dari penelitian ini sebagai bentuk dokumentasi dalam pegembangan sistem informasi manajemen pengelolaan sampah secara digital. Sistem yang dikembangkan berbasis android flutter menggunakan metode xtreme programming terdiri dari perancanaan sistem, analisis sistem, perancangan sistem, implementasi sistem dan pengujian sistem.  Hasil pengujian blackbox menunjukkan bahwa sistem dapat bekerja dengan baik dengan skor validasi 100%, dan pengujian UAT mendapatkan skor 90.8%, menandakan bahwa aplikasi memenuhi kebutuhan pengguna akhir.
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.
Penalaran Logika menggunakan Scratch pada SD Negeri Pendrikan Lor 03 Winarsih, Nurul Anisa Sri; Rohman, Muhammad Syaifur; Saraswati, Galuh Wilujeng; Mulyanto, Edy; Mardiantara, Naya Alifiah az Azar Putri
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 2 (2024): MEI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i2.2258

Abstract

Kemampuan berpikir kritis dan logis sangat penting untuk perkembangan kognitif siswa di era perubahan zaman yang cepat. Meskipun hasil Program for International Student Assessment (PISA) menunjukkan bahwa kemampuan berpikir kritis siswa di Indonesia masih rendah, namun potensi peningkatan dapat terlihat. Indonesia memiliki high equity meskipun berada pada kuadran low performance, menunjukkan kesempatan untuk pengembangan lebih lanjut. Kemampuan berpikir logis melibatkan analisis masalah secara sistematis, yang dapat ditingkatkan melalui model pembelajaran yang tepat. Dalam konteks ini, Scratch, sebuah platform pemrograman visual, ditunjukkan sebagai solusi yang menarik karena memadukan pengalaman belajar dengan aktivitas yang menyenangkan bagi anak-anak. Dalam penelitian ini, Scratch digunakan untuk melatih penalaran logika pada siswa sekolah dasar. Hasilnya para siswa dapat lebih baik mengaplikasikan konsep logika dalam menyelesaikan masalah melalui pembuatan game di Scratch. Oleh karena itu, pengenalan pemrograman komputer sejak dini melalui platform seperti Scratch sangat direkomendasikan sebagai upaya untuk mempersiapkan generasi mendatang dalam menghadapi perkembangan teknologi yang pesat.
Penerapan Arsitektur MVVM Pada Aplikasi Tanamin Untuk Mendeteksi Penyakit Tanaman Berbasis Android Saraswati, Galuh Wilujeng; Febrianto, Nanang
Progresif: Jurnal Ilmiah Komputer Vol 19, No 2: Agustus 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v19i2.1347

Abstract

Indonesia is an agrarian country or a country with a population that makes a living as farmers. Many problems experienced by farmers, especially in the handling of plant diseases. This application is a solution in preventing and dealing with plant diseases. The use of technology in developing applications can help with the problems of various sectors, especially in this case agriculture. This application development uses the MVVM (Model View -View Model) architecture and uses the Extreme Programming (XP) method. The use of the MVVM architecture was chosen to make it easier when maintaining applications because the development is separated between the interface and business logic. The main feature of this application is to detect plant diseases by taking pictures via a smartphone camera. Then the image will be sent to the cloud server to process disease detection by implementing the REST API using Retrofit. From the results of the Black Box testing carried out, the entire system works well according to the test scenario carried out.Keywords: MVVM; Tanamin; Retrofit; Blackbox; Extreme Programming AbstrakIndonesia merupakan negara agraris atau negara dengan penduduk yang bermata pencaharian sebagai petani. Banyak permasalahan yang dialami para petani khususnya dalam penanganan penyakit tanaman. Aplikasi ini menjadi solusi dalam mencegah dan menangani penyakit tanaman. Pemanfaatan teknologi dalam mengembangkan aplikasi dapat membantu permasalahan berbagai sektor khususnya dalam hal ini adalah pertanian. Pengembangan aplikasi ini menggunakan arsitektur MVVM (Model View -View Model) dan menggunakan metode Extreme Programming (XP). Penggunaan arsitektur MVVM dipilih agar memudahkan pada saat pemeliharaan aplikasi karena pengembangannya dipisahkan antara antarmuka dan logika bisnis. Fitur utama pada aplikasi ini adalah untuk mendeteksi penyakit tanaman dengan cara melakukan pengambilan gambar melalui kamera smartphone. Selanjutnya gambar tersebut akan dikirimkan ke server cloud untuk melakukan pemrosesan deteksi penyakit dengan menerapkan REST API menggunakan Retrofit. Dari hasil pengujian Black Box yang dilakukan seluruh sistem bekerja dengan baik sesuai dengan skenario tes yang dilakukan.Kata kunci: MVVM; Tanamin; Retrofit; Blackbox; Extreme Programming
Peningkatan Urgensi Daerah Rawan Bencana melalui Analisis Geoparsing pada Berita Kebencanaan dengan Text Mining Rohman, Muhammad Syaifur; Sri Winarsih, Nurul Anisa; Saraswati, Galuh Wilujeng
Progresif: Jurnal Ilmiah Komputer Vol 19, No 2: Agustus 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v19i2.1295

Abstract

This research aims to enhance the Disaster Vulnerability Map through the utilization of Geoparsing method by Text Mining on disaster news reports. The increase in casualties and damages caused by natural disasters reported by BNPB from 2020 to 2021 necessitates effective disaster management and preparedness for future events. BPBD Jawa Tengah employs disaster news reports as a means to raise public awareness. However, the creation of an accurate Disaster Vulnerability Map requires geospatial data on the frequency of disaster occurrences, which is not available within the reports. Thus, Geoparsing is employed to process the disaster reports data. The findings of this study demonstrate that Geoparsing can enhance the accuracy of the Disaster Vulnerability Map and provide insights into the level of urgency for disaster preparedness in the Preparedness Disaster Management phase.Keywords: Text Mining; Geoparsing; Disaster Prone Area; Disaster Management AbstrakPenelitian ini bertujuan untuk meningkatkan Peta Rawan Bencana melalui penggunaan metode Geoparsing yang didapat melalui Text Mining pada berita laporan kebencanaan. Dalam kurun waktu tahun 2020 hingga 2021, terjadi peningkatan korban dan kerugian akibat bencana alam yang dilaporkan oleh BNPB. Oleh karena itu, penanganan dan persiapan yang efektif diperlukan untuk mengurangi dampak bencana di masa depan. BPBD Jawa Tengah menggunakan berita laporan kebencanaan sebagai upaya untuk meningkatkan kesadaran masyarakat. Namun, untuk menghasilkan Peta Rawan Bencana yang akurat, diperlukan data geospasial mengenai frekuensi kejadian bencana yang tidak tersedia dalam laporan tersebut. Dalam penelitian ini, dilakukan pengolahan data laporan kebencanaan menggunakan metode Geoparsing. Hasil penelitian menunjukkan bahwa Geoparsing dapat meningkatkan akurasi Peta Rawan Bencana dan memberikan informasi mengenai tingkat urgensi persiapan terhadap bencana di fase Preparedness Disaster Management.Kata kunci: Text Mining; Geoparsing; Disaster Prone Area; Disaster Management
Pengembangan Aplikasi Dewan Masjid Indonesia (DMI) Berbasis Ekonomi Umat Dengan Metode Waterfall Winarsih, Nurul Anisa Sri; Fanani, Ahmad Zainul; Saraswati, Galuh Wilujeng; Rohman, Muhammad Syaifur
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.379

Abstract

The conventional economic system has been developing for a long time, followed by the Islamic economic system which is currently developing rapidly in Indonesia. Sharia economic activities are based on the rules of the Koran regarding right and wrong, good and bad, and halal and haram rules. The background of sharia business ethics is the Prophet Muhammad SAW which is based on the Al-Quran and Hadith. The sharia economy can be seen with the emergence of Islamic banks, almost all major banks in Indonesia have sharia branches. Not only that, e-money and sharia payment gateways already exist. This is based on the large number of Indonesian people who adhere to Islam and the increasing awareness of Muslims in Indonesia in implementing the Islamic economic system. Since March 2, 2020, the Corona virus or Covid-19 pandemic has entered Indonesia. Many employees become unemployed. 50% of MSMEs could go bankrupt in the next few months. Whereas small businesses make a big contribution to the absorption of jobs in Indonesia which creates income for the population. The management of the Semarang City DMI organization took the initiative to ease the burden on the people, especially in the city of Semarang by making the DMI application based on the people's economy. Waterfall is the method used in this research. Waterfall is suitable for application development with a complete needs analysis. After the PSBB period ended, there were many unemployed who tried their luck by selling small businesses and MSMEs opened slowly. It is hoped that the DMI application can introduce the business of the community around the mosque and other people can buy the business easily through the application.
Co-Authors Achmad Naila Muna Ramadhani Adhitya Nugraha Aditya Wahyu Ramadhan Adji, Dian Restu Ahmad Zainul Fanani Ajib Susanto Akbar Dwi Syahputra Angga Apriano Hermawan Azhara Devi Sandi Azzahra, Tarissa Aura Bagas Aditya Mahendra Cahyani, Salsabila Nida Danny Oka Ratmana Dianna Yanuaresta Dwi Puji Prabowo, Dwi Puji Erba Lutfina Etika Kartikadarma Fafaza, Safira Alya Fakhrurrozi Fakhrurrozi, Fakhrurrozi Febrianti, Ervina Febrianto, Nanang Filmada Ocky Saputra Filmada Ocky Saputra Guruh Fajar Shidik Gustina Alfa Trisnapradika Handoyo, Dhiky Resandi Wur Heru Agus Santoso Lakui Johary Lutfina, Erba Malik Aziz Ali Mardiantara, Naya Alifiah az Azar Putri Megantara, Rama Aria Meilani Dwi Permatasari Miranti Alysha Zulia Larasati Muhamad Ni'am Syukri Roni Asmi Muhammad Syaifur Rohman Muhammad Syaifur Rohman Muljono, - Mulyanto, Edy Nur Inayati Nurun Najmi Amanina Pergiwati, Dewi Prashanti, Eva Pulung Nurtantio Andono Rahmat Trinanda Pramudya Amar Rama Tri Agung Ramadhan, Aditya Wahyu Ramadhani, Irfan Wahyu Ratmana, Danny Oka Renjiro Azhar Pramono Ricardus Anggi Pramunendar Rino Agung Rizky Syah Gumelar Rohman, Muhammad Syaifur Rohman, Muhammad Syaifur Saputra, Filmada Ocky Sri Winarsih, Nurul Anisa Wildan Mahmud Winarsih, Nurul Anisa Sri Yustiqomah, Evita Citra