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All Journal Elkom: Jurnal Elektronika dan Komputer Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Indonesian Journal on Software Engineering (IJSE) Jurnal Khatulistiwa Informatika Jurnal Informatika Universitas Pamulang Antivirus : Jurnal Ilmiah Teknik Informatika JATI (Jurnal Mahasiswa Teknik Informatika) Computer Science (CO-SCIENCE) Jurnal Teknik Informatika Journal of Artificial Intelligence and Digital Business Akademika
Diantika, Sri
Universitas Bina Sarana Informatika
Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences

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Perancangan Sistem Informasi Rental Mobil Berbasis Website pada Rentalin Aja Supriyadi, Riki; Maulidah, Nurlaelatul; Nalatissifa, Hiya; Fauzi, Ahmad; Diantika, Sri
Indonesian Journal on Software Engineering Vol 10, No 2 (2024): IJSE 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijse.v10i2.24569

Abstract

Abstrak Rental mobil merupakan layanan penyewaan kendaraan yang dapat digunakan dengan atau tanpa sopir untuk jangka waktu harian atau mingguan. Dalam menghadapi persaingan yang semakin ketat, pelaku usaha pada bidang ini dituntut untuk terus berinovasi, terutama dalam meningkatkan kualitas pelayanan, keamanan, kenyamanan, dan kemudahan bagi konsumen. Banyak usaha rental mobil yang masih menggunakan sistem manual, di mana pelanggan harus datang langsung atau menghubungi via telepon untuk memeriksa ketersediaan mobil dan pelanggan perlu melakukan booking dengan membayar uang muka, sementara pihak rental masih mencatat data pemesanan dan peminjaman secara manual di buku. Penelitian ini bertujuan untuk merancang sebuah sistem informasi berbasis web yang mempermudah konsumen dalam mendapatkan informasi detail terkait kendaraan yang tersedia di Rentalin Aja. Melalui sistem ini, konsumen dapat mengetahui spesifikasi, harga sewa, serta gambar kendaraan yang tersedia tanpa harus datang langsung ke lokasi. Selain memberikan kemudahan bagi konsumen, website ini juga dirancang untuk membantu staf perusahaan dalam mengelola pemesanan dan menyusun laporan dengan lebih cepat dan akurat. Sistem ini dikembangkan menggunakan metode waterfall, yang meliputi tahapan analisis kebutuhan, perancangan, implementasi, pengujian, hingga pemeliharaan. Dengan adanya website ini, Rentalin Aja diharapkan mampu memberikan pengalaman yang lebih baik kepada konsumen serta meningkatkan efisiensi operasional perusahaan, sehingga mampu bersaing di industri rental mobil yang semakin kompetitif. Kata kunci: Website, Rental Mobil, Waterfall Abstract Car rental is a vehicle leasing service that can be used with or without a driver for daily or weekly periods. In facing increasingly fierce competition, business players in this field are required to constantly innovate, particularly in improving service quality, security, comfort, and convenience for customers. Many car rental businesses still rely on manual systems, where customers must visit the location or call to check vehicle availability, make bookings with a down payment, and have their booking and rental data manually recorded in a ledger. This study aims to design a web-based information system to facilitate consumers in accessing detailed information about the vehicles available at Rentalin Aja. Through this system, customers can view vehicle specifications, rental prices, and images without having to visit the location. In addition to simplifying the consumer experience, this website is also designed to assist staff in managing bookings and compiling reports more quickly and accurately. The system is developed using the waterfall method, which includes stages of requirements analysis, design, implementation, testing, and maintenance. With this website, Rentalin Aja is expected to provide a better customer experience while improving the company's operational efficiency, enabling it to compete in the increasingly competitive car rental industry. Keywords: Website, Car Rental, Waterfall
KLASIFIKASI DATA MINING UNTUK MENGESTIMASI POTENSI CURAH HUJAN MENGGUNAKAN ALGORITMA NAIVE BAYES Nuris, Nuzuliarini; Salsabila, Nina Nur; Rifandi, Julian; Diantika, Sri
JURNAL AKADEMIKA Vol 17 No 1 (2024): Jurnal Akademika
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/akademika.v17i1.1320

Abstract

One of the things that is very important in Indonesia is climate. Climate itself greatly influences human survival. The relationship between rainfall and climate is very close and complex, where climate change can affect rainfall patterns in an area. High or low rainfall can have negative impacts on the environment and agriculture. Therefore, a deep understanding of rainfall patterns is very important in development planning and natural resource management. With the classification of potential rainfall, it is hoped that it can provide accurate predictions and assist in planning and managing related risks. The classification used in this research is Naive Bayes with monthly rainfall data in East Jakarta City from 2019 - 2022. Based on the calculation results, an accuracy rate of 80% was obtained, either using manual calculations or using the RapidMiner application.
IMPLEMENTATION OF MULTI-CLASS GRADIENT BOOSTING TO CLASSIFY ANIMAL SPECIES IN ZOOS: IMPLEMENTASI MULTI-CLASS GRADIENT BOOSTING UNTUK MENGKLASIFIKASIKAN JENIS HEWAN PADA KEBUN BINATANG Sri Diantika; Hiya Nalatissifa; Riki Supriyadi; Nurlaelatul Maulidah; Ahmad Fauzi
Antivirus : Jurnal Ilmiah Teknik Informatika Vol 17 No 1 (2023): Mei 2023
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/antivirus.v17i1.2812

Abstract

Animals are one of the living things that have various types. Grouping types of animals based on similarities and differences in characteristics owned is one of the important activities carried out To make it easier to compare, recognize, study certain types of animals and be able to find out kinship relationships between animals, So if a new type of animal is found that does not yet have a name, it will be easier for us to give a name to the animal based on the type and based on the group. In research on the classification of animal species in zoos that have multi-class, the best classification is obtained by applying gradient boosting parameters with n_estimators of 50, max_depth 3, sub-sample of 1.0, learning rate of 0.1, and using criterion friedman Mse. And by implementing Split validation or division between training data by 80% for training data and 20% for testing data. The results stated that the proposed model was better than some other models that had also been tested with an accuracy value of 93.75%, recal of 94%, precision of 96% and MSE to measure the average magnitude of error in a series of classifications of 12.5%, the smaller the MSE value, the better it would be in classifying.
PREDIKSI KUALITAS AIR MENGGUNAKAN METODE RANDOM FOREST, DECISION TREE, DAN GRADIENT BOOSTING Maulidah, Nurlaelatul; Maulidah, Mawadatul; Supriyadi, Riki; Nalatissifa, Hiya; Diantika, Sri; Fauzi, Ahmad
Jurnal Khatulistiwa Informatika Vol 12, No 1 (2024): Periode Juni 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jki.v12i1.16004

Abstract

Air merupakan sumber daya alam yang sangat penting dan menjadi suatu kebutuhan pokok bagi kelangsungan mahluk hidup baik manusia, hewan dan tumbuhan, namun tidak semua air aman untuk dikonsumsi, sehingga diperlukan adanya identifikasi kualitas air yang aman untuk di konsumsi. Memperkirakan kualitas air telah menjadi salah satu tantangan signifikan yang dihadapi dunia dalam beberapa dekade terakhir. Penelitian ini menyajikan model prediksi kualitas air menggunakan tiga algoritma machine learning Decision Tree, Gradient Boosting dan Random Forest, dimana model machine learning tersebut kemudian dievaluasi secara eksperimental dengan menggunakan data water_potability dari kaggle. Ketiga algoritma ini akan dilakukan perbandingan pada proses klasifikasi data untuk mengetahui metode mana yang paling akurat, dilihat dari tingkat akurasi yang paling tinggi. Hasilnya menunjukan pada penelitian ini Random Forest menjadi model yang memiliki akurasi dengan nilai akurasi yang tinggi dan akurat sebesar 88.33%, dan untuk Decision Tree dengan nilai akurasi 80,83% dan Gradient Boosting memiliki akurasi terendah yaitu 73,33%. Sehingga pada penelitian yang dilakukan Random Forest menjadi algoritma paling akurat dan baik untuk digunakan pada dataset water_potability.
Prediksi Harga Properti Di Indonesia Menggunakan Algoritma Random Forest Fauzi, Ahmad; Maulidah, Nurlaelatul; Supriyadi, Riki; Nalatissifa, Hiya; Diantika, Sri
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 1 (2025): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i1.367

Abstract

Harga properti merupakan aspek yang dipengaruhi oleh berbagai faktor, seperti lokasi, luas tanah, jumlah kamar, aksesibilitas, serta kondisi ekonomi di suatu wilayah. Prediksi harga properti yang akurat sangat penting bagi pengembang, investor, dan calon pembeli dalam mengambil keputusan yang tepat. Penelitian ini menerapkan algoritma Random Forest untuk memprediksi harga properti berdasarkan dataset sintetis yang mencakup 10.000 sampel properti dari berbagai kota di Indonesia. Data dianalisis dengan mempertimbangkan faktor utama yang memengaruhi harga, seperti luas tanah, luas bangunan, jumlah kamar tidur, jarak ke pusat kota, serta fasilitas terdekat. Model dievaluasi menggunakan Mean Absolute Error (MAE) dan Root Mean Squared Error (RMSE), dengan hasil MAE sebesar Rp2,48 miliar dan RMSE sebesar Rp2,89 miliar, yang menunjukkan tingkat akurasi yang cukup baik. Analisis lebih lanjut mengungkap bahwa faktor jarak ke pusat kota merupakan variabel paling berpengaruh terhadap harga properti, diikuti oleh luas tanah dan luas bangunan. Hasil penelitian ini menunjukkan bahwa algoritma Random Forest dapat digunakan sebagai alat bantu dalam menentukan harga properti dengan akurasi yang lebih baik dibandingkan metode tradisional. Studi ini juga mengusulkan pengembangan lebih lanjut dengan mempertimbangkan faktor tambahan, seperti indeks pembangunan wilayah dan tren harga historis, untuk meningkatkan ketepatan prediksi.
Penerapan Teknik Random Oversampling Untuk Memprediksi Ketepatan Waktu Lulus Menggunakan Algoritma Random Forest Diantika, Sri; Nalatissifa, Hiya; Maulidah, Nurlaelatul; Supriyadi, Riki; Fauzi, Ahmad
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.1996

Abstract

Punctuality of graduation is something that students yearn for, besides being important for students, punctuality of graduation is also very important for universities, this is because the aspect of student graduation is one aspect of assessment in an institutional accreditation process of a university to show its quality. One of the obstacles faced to find out whether a student can graduate on time or not is because the study period cannot be detected early, this will have an impact on late student graduation. To analyze this, a lot of research was conducted on the accuracy of student graduation, through the cumulative grade point average (GPA) obtained by students during their studies. This research on the prediction of student graduation timeliness uses a random forest algorithm model. The data used in this research object has an unbalanced number of data classes, to overcome this, a random oversampling (ROS) resampling technique is applied and also applies Split validation or division between learning data by 50% for test data and 50%. To evaluate the model built, the author uses evaluation metrics such as accuracy, recall, and precision. The results of the study showed that the proposed model can well predict compared to other models, namely with the results precision of 87.05%, accuracy test values of 90.04%, recall of 90.04%.From these results, it can be interpreted that the random forest algorithm is considered good in predicting the timeliness of a student's graduation
Co-Authors AA Sudharmawan, AA Agus Subekti Ahmad Fauzi Ahmad Fauzi Ahmad Fauzi Akrom, Akrom Aprilia Kartini, Kasih Dafa, Muhammad Fauzi Ahmad Muda Gata, Windu Hiya Nalatissifa Hiya Nalatissifa Khoirun Nisa Mareanus Lase Mawadatul Maulidah Nurlaelatul Maulidah Nuzuliarini Nuris Rifandi, Julian Riki Supriyadi Riki Supriyadi Salsabila, Nina Nur Silvia, Hana Windu Gata