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All Journal Bulletin of Electrical Engineering and Informatics Nuansa Informatika Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Ilmiah Universitas Batanghari Jambi JURNAL MEDIA INFORMATIKA BUDIDARMA CogITo Smart Journal Jurnal Informatika Universitas Pamulang JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) ILKOM Jurnal Ilmiah JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Technologia: Jurnal Ilmiah Aisyah Journal of Informatics and Electrical Engineering Indonesian Journal of Business Intelligence (IJUBI) bit-Tech Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Respati Jurnal Abdi Insani Journal of Computer System and Informatics (JoSYC) Jurnal Graha Pengabdian Infotek : Jurnal Informatika dan Teknologi jurnal syntax admiration TEPIAN Jurnal Teknologi Informatika dan Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia JNANALOKA SENADA : Semangat Nasional Dalam MengabdI Journal of Electrical Engineering and Computer (JEECOM) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Jurnal Sisfotek Global Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Cerdika: Jurnal Ilmiah Indonesia SENADA : Semangat Nasional Dalam Mengabdi Intechno Journal : Information Technology Journal The Indonesian Journal of Computer Science SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Jurnal Teknik AMATA Jurnal TAM (Technology Acceptance Model)
Kusnawi Kusnawi
Universitas Amikom Yogyakarta
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Exploratory Data Analysis Faktor Pengaruh Kesehatan Mental di Tempat Kerja Raissa Maringka; Kusnawi Kusnawi
CogITo Smart Journal Vol 7, No 2 (2021): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v7i2.312.215-226

Abstract

Gangguan kesehatan mental pada lingkungan kerja merupakan hal yang sering ditemui di kalangan para pengerja. Menurut prediksi World Health Organization (WHO) pada tahun 2011 menyatakan bahwa pada tahun 2030, depresi akan menjadi penyakit yang membebankan dunia. Mempertahankan lingkungan pekerjaan yang bebas stres merupakan faktor yang penting untuk memiliki karyawan yang lebih produktif dalam bekerja. Untuk itu, mendeteksi gangguan kesehatan mental lebih awal merupakan hal yang penting harus dilakukan. Pada penelitian ini, peneliti mengambil data yang bersumber dari OSMI (Open Sourcing Mental Illness). Dataset ini mencakup data para pengerja secara per orangan yang berhubungan dengan bagaimana pekerjaan mereka mempengaruhi kesehatan mental. Peneliti berharap dengan exploratory data analysis berbasis python pada faktor-faktor yang mempengaruhi kesehatan mental di lingkungan kerja dapat membantu dalam mengevaluasi suatu perusahaan atau lingkungan pekerjaan dalam menolong para karyawan atau pengerja untuk lebih produktif dan sehat secara mental ataupun fisik. Pendekatan ini juga diharapkan dapat membantu para manajer atau HR untuk lebih mengerti akan kebutuhan karyawan serta mengambil langkah untuk mencegah masalah yang dapat mempengaruhi kesehatan mental para karyawan
Analisa Perkembangan Musik Pada Spotify Menggunakan Structured Query Language (SQL) Raissa Camilla Maringka; Aulia Khoirunnita; Rodney Maringka; Ema Utami; Kusnawi Kusnawi
CogITo Smart Journal Vol 7, No 1 (2021): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v7i1.287.1-14

Abstract

Musik mengalami perubahan dan berevolusi hingga mencapai abad ke 21. Tidak seperti jaman purbakala, generasi digital saat ini dapat menggunakan teknologi dalam menikmati musik. Spotify menjadi salah satu aplikasi yang banyak digunakan sebagai platform dalam music streaming. Perubahan musik yang signifikan setiap tahunnya mempengaruhi pembentukan pola pikir masyarakat terhadap preferensi pilihan musik. Oleh karena itu perlu dilakukan pemantauan terhadap trend perkembangan musik serta mengetahui faktor-faktor yang mempengaruhinya untuk melihat perubahan apa saja yang terjadi serta menjadi tolak ukur untuk membantu industri musik dalam menghasilkan musik yang layak didengarkan serta membawa pengaruh positif. Penelitian ini akan memberikan hasil analisa dari perkembangan trend perkembangan musik khususnya pada aplikasi Spotify menggunakan Structured Query Language. Dari hasil analisa didapatkan visualisasi dari trend genre musik dan fitur audio dalam jangkauan tahun 2010 hingga 2020 yang diolah menggunakan Power BI. Diharapkan hasil tersebut dapat membantu indusri musik dalam menghasilkan musik yang digemari serta memberikan pengetahuan yang baik terhadap penggemar musik.Kata kunci—Musik, Spotify, SQL, Power BI
Studi Literatur: Perbandingan Basis Data NewSQL Omar Muhammad Altoumi Alsyaibani; Kusnawi; Ahmad Yusuf; Muhammad Reza Riansyah
JNANALOKA Vol. 02 No. 01 Maret Tahun 2021
Publisher : Lentera Dua Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36802/jnanaloka.2021.v2-no1-1-12

Abstract

Selama bertahun-tahun sistem basis data relasional menjadi solusi untuk penyimpanan data sampai datang era Big Data. Kemudian sistem basis data non-relasional (yang dikenal sebagai sistem basis data NoSQL) mulai dikembangkan sebagai solusi basis data yang mempunyai kecepatan tinggi dan scalable untuk menangani volume data yang terus meningkat. Di sisi lain, NoSQL mempunyai beberapa kekurangan, diantaranya : tidak mendukung ACID (Atomicity, Consistency, Isolation, Durability) dan beberapa sistem database NoSQL tidak mendukung bahasa kueri terstruktur (SQL). NewSQL merupakan pengembangan dari NoSQL yang memiliki dukungan terhadap terhadap ACID dan bahasa SQL. Sistem basis data NewSQL yang dikembangkan saat ini memiliki arsitektur, fitur dan implementasi yang bervariasi. Penelitian ini melakukan ulasan ringkas (quick review) pada perkembangan sistem basis data mulai dari relasional yang tradisional, NoSQL hingga NewSQL dengan dekripsi lebih pada sistem basis data NewSQL. Pada bagian akhir dilakukan perbandingan beberapa database NewSQL pada lima aspek, diantaranya : Ease of Use, Features & Functionality, Advanced Features, Integration dan Performance. Dari hasil perbandingan, pada aspek Ease of Use c-treeACE mendapat nilai tertinggi sedangkan Apache Trafodion menjadi yang terendah. Pada aspek Features & Functionality TIBCO ActiveSpaces menjadi yang tertinggi sedangkan VoltDB mendapat skor terendah. Selanjutnya, Altibase men- dapat skor tertinggi pada aspek Advanced Features dan Apache Trafodion kembali menjadi yang terendah. Pada aspek Integration, skor tertinggi ada pada CockroachDB dan terendah ada pada Pivotal GemFire XD. Terakhir, pada aspek Performance, ActorDB menjadi yang tertinggi dan Apache Trafodion menjadi yang terendah lagi.
IMPLEMENTASI SINKRONASI DATABASE SQL DENGAN REPLIKASI PADA APLIKASI FINANCE PT.XYZ Muhammad Firdaus Abdi; Agung Susanto; Ema Utami; Kusnawi Kusnawi
Technologia : Jurnal Ilmiah Vol 12, No 2 (2021): Technologia (April)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (626.428 KB) | DOI: 10.31602/tji.v12i2.4577

Abstract

Database atau basisdata pada perkembangannya harus dikelola dengan baik, apalagi saat ini database yang didalamnya memiliki informasi data penting sebuah perusahaan yang harus dilindungi dari hilangnya data sehingga diperlukannya sistem untuk membackup data tersebut. Disisi lain ketika sebuah aplikasi yang dimiliki perusahaan memiliki dua tempat site A dan site B yang dimana saling terhubung sehingga diperlukan sistem untuk menangani ketika satu tempat mengalami gangguan aplikasi itu dapat berkerja dan ketika sistem sudah berjalan baik data pada site A dan site B memiliki data yang sama. Penelitian ini mencoba mengimplementasikan sistem replikasi data, yang dimana data site A dan site B akan dihubungkan, selanjutnya akan dilakukan replikasi data dengan sistem sinkronisasi database. Hasil dari implementasi ini berhasil dilakukan akan tetapi untuk memastikan proses replikasi data berhasil dilakukan pengujian lain seperti penghapusan data, edit data, ubah data, dari pengujian ini semua berhasil sinkron antara site A dan site B sehingga data yang dimiliki sama antar site, pengujian yang dilakukan diolah dalam bentuk persentasi keberhasilan sehingga didapat proses sinkronisasi database dengan replikasi data 100%, untuk itu replikasi ini dapat terapkan untuk proses bisnis pada perusahaan PT. XYZ dalam penanganan sistem database yang dikelola. Kata Kunci -      SQL Server, Replikasi Data, Database, Data, SQL
Evaluasi Hasil Pengujian Tingkat Clusterisasi Penerapan Metode K-Means Dalam Menentukan Tingkat Penyebaran Covid-19 di Indonesia Elsa Virantika; Kusnawi Kusnawi; Joang Ipmawati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4325

Abstract

Coronavirus Diseases 2019, often known as COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Indonesia has a large area so that it is easy to contract COVID-19 and the spread of the Covid-19 virus in Indonesia is growing quite rapidly. Based on the region in Indonesia, it can be grouped into parts of the provinces in Indonesia and generate provincial points for the distribution of Covid-19 cases, aiming to create a strategy for handling the spread of COVID-19 in all provinces in Indonesia. The grouping of the level of spread of COVID-19 is carried out using a data mining method, namely the k-means clustering algorithm by grouping data into several clusters based on the similarity of the data. Based on the results of the study, 3 clusters were identified, namely cluster 0 with a low level of distribution of Covid-19, 12 provinces, cluster 1 with a moderate level of distribution of COVID-19, 18 provinces, and cluster 2 with a high level of distribution of COVID-19, 4 categories. province. Based on the results of this study, it is hoped that it can provide information and support the government to make strategic decisions in each cluster to reduce the level of spread of COVID-19 in Indonesia.
Deteksi Hama Pada Daun Apel Menggunakan Algoritma Convolutional Neural Network Dede Husen; Kusrini Kusrini; Kusnawi Kusnawi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4667

Abstract

Today the need for fruit consumption is increasing along with the increasing human population and awareness of the consumption of nutritious foods, apples are one of the most consumed fruits by humans worldwide. According to data quoted from the Indonesian National Statistics Center in 2021, apple production in 2021 decreased from the previous year from 519,531 tons to 509,544 tons. One of the causes of the decline in apple production is the presence of pests on the apple plant. At least there are several types of pests that can be identified on apple leaves, namely Apple Scrub (Venturia inaequalis), Apple Black Root (Botryosphaeria) and Apple Cedar/Rust (Gymnosporangium juniperi virginianae). The research stage begins with conducting several literature studies regarding related research, then formulating and validating the problem and starting to collect data from the Kaggle public dataset. Then in the experimental stage, the author divides the dataset into three parts with a percentage of 80% training data, 10% validation data and 10% testing data. The image classification method used is the Convolutional Neural Network (CNN) algorithm to create a model that can classify image data, the process of implementing the author uses the python programming language to build the model. The author conducted several experiments by making changes to several model parameters that affect the accuracy of the model. To evaluate the performance and accuracy of the model using a confusion matrix. The results of the study indicate that image size, data augmentation and the number of epochs greatly affect the accuracy of the model, from the test results the CNN model with the best accuracy is the model with the image size parameter 256x256, horizontal flip, vertical flip and random rotation data augmentation and the number of the 60th epoch has the highest accuracy rate of 99.66%. The results of this study are expected to be implemented in an application that can be used directly by farmers in detecting pests on apple plants quickly and accurately.
Analisis Perbandingan Kinerja Algoritma Klasifikasi dengan Menggunakan Metode K-Fold Cross Validation Ritham Tuntun; Kusrini Kusrini; Kusnawi Kusnawi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4681

Abstract

This study aims to compare the performance of two classification data mining algorithms, namely the K-Nearest Neighbor algorithm, and C4.5 using the K-fold cross validation method. The data used in this study are iris public data with a total of 150 data and 3 label target classes, namely iris-setosa, iris-versicolor, and iris-virginica. The training data used is 97% or 145 data from 150 data, and the testing data used is 3% or 5 data, and the number of K in the K-fold cross validation is 30 or 30 times the experimental stage. The results showed that the performance of the K-Nearest Neighbor algorithm was 95.33%, recall was 95.33%, and precision was 96.27%. While the C4.5 algorithm obtained an accuracy of 96.00%, recall of 94.44%, and precision of 93.52%.
Classification using the C4.5 Algorithm in Predicting Students Organizational Status Amikom University Yogyakarta Neni Firda Wardani Tan; Kusnawi Kusnawi
Sistemasi: Jurnal Sistem Informasi Vol 11, No 3 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v11i3.2054

Abstract

Participation in an organization as a college student is an important way to self-improvement. This research analyzes students' learning in an organization and whether the student is active or not active in an organization using the Decision Tree C4.5 algorithm. Attributes analyzed were out-of-campus organization, previous organization experience, public speaking, problem-solving, confidence level, and personality. From a question from propagating on October 2021, the researcher collect 203 raw data, with 167 processes ones that were used in this research. The test conducted results in a tree or the decision tree that could be used to decide how active a student is in an organization. The accuracy value of this test using cross-validation resulted in a score of 59.27% or in comparison to the data training: data testing of 4:1 or 80%: 20%.
SMARTPHONE RECOMMENDATION SYSTEM USING MODEL-BASED COLLABORATIVE FILTERING METHOD Fajar Aji Prayoga; Kusnawi Kusnawi
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.6.413

Abstract

Smartphone are now an importan item that is needed by many people. The rapid development of technology make smartphone companies are competing to release their best smartphones.The many smartphones in online shop cause user to become disoriented about their choice. A recommendation system can help the user in choosing the smartphone that the user likes. In this study, a recommendation system was made using the collaborative filtering method with the K-Nearest Neighbors algorithm and combined with the application of K-Means algorithm to divide the smartphone into several group. The output of collaborative filtering method is that the model can give smartphone rating predictions to user. The prediction results will be used as the basis for giving recommendations to user. The purpose of smartphones groupping is so that the recommendation results are more specific and accurate. The evaluation of the model gets an MAE value is 1.1047 and RMSE value is 1.7579. So it can be concluded that the development of a smartphone recommendation system was successfully implemented.
Sentiment Analysis and Classification of Forest Fires in Indonesia Indra Irawanto; Cynthia Widodo; Atin Hasanah; Prema Adhitya Dharma Kusumah; Kusirini Kusrini; Kusnawi Kusnawi
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1337.175-185

Abstract

Twitter is a well-known social media platform since it allows users to retweet, leave comments, exchange the latest information, and even find out about forest fires. However, no one has processed Twitter data in the form of the topic of forest fires. Despite the fact that this information is incredibly important for determining how much people care about sharing this knowledge and this phenomenon. Hence, one of the efforts in managing Twitter data in the form of text is using NLP (Natural Language Processing) which is now starting to be widely discussed. In addition, the use of word weighting utilizing Vader will also be used in this process. Furthermore, the use classifying process is conducted using 3 kinds of algorithms including Naïve Bayes, Random Forest and SVM (Support Vector Machine). The results of this study, the accuracy obtained from each method has not reached 90%. The Precision, Recall and F1-Score values have also not reached 90%.
Co-Authors Abdulloh, Ferian Fauzi Afrig Aminuddin Agung Susanto Agung Susanto Ahmad Fauzi Ahmad Sanusi Mashuri Ahmad Yusuf Ainnur Rafli Ainul Yaqin Ali Mustopa, Ali Alva Hendi Muhammad Andi Sunyoto Andi Sunyoto Anggit Dwi Hartanto, Anggit Dwi Arief Setyanto Arifuddin, Danang Arnila Sandi Aryawijaya Asadulloh, Bima Pramudya Assani, Moh. Yushi Atin Hasanah Atin Hasanah Atmoko, Alfriadi Dwi Aulya, Fiola Utri BAYU SATRIYA, RIYAN Bhahari, Rifqi Hilal Candra Rusmana Cynthia Widodo Cynthia Widodo Dede - Sandi Dede Husen Dede Sandi Dewi Kartika Dimaz Arno Prasetio Elsa Virantika Ema Utami Erna Utami Fachri Ardiansyah Fajar Abdillah, Moh Fajar Aji Prayoga Haris, Ruby Hartatik Haryo, Wasis Hasirun Hasirun Hendrik Hendrik Henri Kurniawan Hidayatunnisa'i Indra Irawanto Joang Ipmawati Kanoena, Melcior Paitin Karisma Septa Kresna Khairullah, Irfan Khalil Khoirunnita, Aulia Khrisna Irham Fadhil Pratama Kusirini Kusrini Kusrini KUSRINI Kusrini Kusrini - - Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini, Kusrini Luthfi Nurul Huda M Andika Fadhil Eka Putra M. Nurul Wathani Maehendrayuga, Arief Majid Rahardi Maringka, Raissa Muh. Syarif Hidayatullah Muhammad Firdaus Abdi Muhammad Firdaus Abdi Muhammad Irvan Shandika Muhammad Reza Riansyah Nadhira Triadha Pitaloka Nayoma, Fisan Syafa Neni Firda Wardani Tan Ni’matur Rohim Nurul Zalza Bilal Jannah Nurus Sarifatul Ngaeni Omar Muhammad Altoumi Alsyaibani Pattimura, Yudha Bagas Pebri Antara Pramono, Aldi Yogie Prastyo, Rahmat Prema Adhitya Dharma Kusumah Puji Prabowo, Dwi Qurniaty, Charlen Alta Raffa Nur Listiawan Dhito Eka Santoso Rahayu, Christa Putri Rifda Faticha Alfa Aziza Rita Wati Ritham Tuntun Rizal Khadarusman Rodney Maringka Sabda Sastra Wangsa Saifulloh Saifulloh Salman Alfaris Salman Alfaris, Salman San Sudirman Sekarsih, Fitria Nuraini Sepriadi - Bumbungan Sepriadi Bumbungan Sri Yanto Qodarbaskoro Sry Faslia Hamka Suyatmi Suyatmi Suyatmi Suyatmi Syaiful Huda Syaiful Ramadhan Tamuntuan, Virginia Taryoko Taryoko Teguh Arlovin Thedjo Sentoso triadin, Yusrinnatul Jinana Van Daarten Pandiangan Virginia Tamuntuan Wahyu Pujiharto, Eka Widyanto, Agung Wirawan, Tegar Yusa, Aldo Yuza, Adela Zaenul Amri