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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Format : Jurnal Imiah Teknik Informatika Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Bina Insani ICT Journal Informatics for Educators and Professional : Journal of Informatics JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Komtika (Komputasi dan Informatika) JSiI (Jurnal Sistem Informasi) JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY INTI Nusa Mandiri JIKA (Jurnal Informatika) Jurnal Sistem Informasi Bulletin of Computer Science Research Journal of Students‘ Research in Computer Science (JSRCS) Jurnal Pengabdian Kepada Masyarakat PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Socius: Social Sciences Research Journal
Henny Leidiyana
Universitas Bina Sarana Informatika
Religion Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Nursing Public Health Social Sciences Veterinary Other

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PERANCANGAN ALAT TIMER TRAFFIC LIGHT MENGGUNAKAN MIKROKONTROLER ATMEGA 8535 BERDASARKAN ANTRIAN JUMLAH KENDARAAN Henny Leidiyana; Muhammad Faisal; Purnamawati Purnamawati
PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer Vol. 5 No. 1 (2018)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (940.132 KB)

Abstract

Bertambah banyaknya kendaraan membuat lalu lintas semakin padat terutama di kota-kota besar. Oleh karena itu dibutuhkan sebuah perangkat yang bisa mengatur durasi nyala lampu lalu lintas secara otomatis berdasarkan jumlah kepadatan kendaraan per jalur. Pada penelitian ini ini dilakukan perancangan alat timer traffic light menggunakan mikrokontroller ATMega 8535 berdasarkan antrian jumlah kendaraan. Perangkat ini menggunakan LED dan sensor LDR sebagai pendeteksi jumlah kepadatan kendaraan lalu lintas dan Mikrokontroller  Code Vision AVR yang diprogram dengan bahasa biner dan heksadesimal. Dari hasil pengujian miniatur pertigaan jalan raya menunjukkan bahwa perangkat mampu mengontrol timer lampu lalu lintas secara otomatis berdasarkan antrian jumlah kendaraan. Jika kendaraan yang terdeteksi melebihi jumlah angka yang ditentukan (padat) yaitu dua maka secara otomatis lampu akan warna hijau dengan durasi waktu 15 detik, jika kepadatan kendaraan sedang maka secara otomatis lampu berwarna hijau dengan durasi lebih sedikit dari yang padat yaitu 10 detik dan terakhir jika lalu lintas kendaraan kosong atau tidak ada kendaran secara langsung lampu berwarna hijau dan durasinya juga lebih sedikit dibandingkan dengan yang sedang yaitu 5 detik.
Klasifikasi pada Citra Bunga dengan Ekstraksi Fitur Color Histogram Muhammad Ghudafa Taufiq Akbar; Henny Leidiyana
FORMAT Vol 12, No 1 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2023.v12.i1.007

Abstract

Perbedaan ciri pada bunga menyebabkan perbedaan spesies bunga sehingga diperlukan pengetahuan untuk dapat mengklasifikasikannya. Bunga dapat dicirikan dari warnanya. Pada bunga dengan jenis yang sama bahkan bisa memiliki beberapa warna yang berbeda. Salah satu teknik untuk mendapatkan pola bisa dilakukan melalui ekstraksi fitur warna. Penelitian ini mencoba untuk melakukan ekstraksi citra dengan teknik color histogram. Penelitian yang menunjukkan efektifitas ekstraksi fitur warna sebelum melakukan klasifikasi sudah cukup banyak dilakukan. Color Histogram adalah teknik yang paling banyak digunakan untuk mengekstraksi fitur warna dari suatu citra karena mewakili gambar dari sudut pandang yang berbeda. Dataset yang digunakan dalam penelitian ini menggunakan citra bunga dengan bentuk yang hampir sama yaitu sunflower, calendula, black eyed susan, common daisy. Pembuatan model klasifikasi terhadap jenis-jenis bunga tersebut menggunakan algoritma pembelajaran mesin Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbor, Linear Discriminant Analysis, Naive Bayes, Support Vector Machine. Selanjutnya Penulis menggunakan Confusion Matrix untuk mengevaluasi model terlatih dan untuk menghasilkan skor akurasi menggunakan 10-fold cross validation. Skor akurasi tertinggi diperoleh dari model Random Forest sebesar 82%.
ENSEMBLE STACKING DALAM ANALISA SENTIMEN REAKSI VETERAN MILITER AS TERHADAP PENGAMBILALIHAN AFGHANISTAN OLEH TALIBAN Henny Leidiyana
INTI Nusa Mandiri Vol 18 No 1 (2023): INTI Periode Agustus 2023
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v18i1.4175

Abstract

Abstrak— Sentiment analysis can be used to glean information about user opinions and identify social or political trends. There have been many studies on sentiment analysis using machine learning or lexicon-based methods that have been quite impressive. However, machine learning models often have difficulty generalizing to new data due to various reasons, such as overfitting and limited training data. These models are also prone to bias and variance, which negatively affect the accuracy of their predictions. This study discusses the application of the ensemble stacking method in sentiment analysis with the topic of the takeover of Afghanistan by the Taliban. By monitoring social media, the author uses a dataset in the form of comments on YouTube news channels related to the topic raised. Several studies have shown how the ensemble stacking method predicts better than the single model. The research was carried out by creating a sentiment classification model with logistic regression machine learning algorithms, SVM, KNN, and CART then the ensemble stacking classifier formed by the base learner of the four algorithms. As a result, for a single classifier, the highest average accuracy is the logistic regression algorithm of 74.6 percent. The four algorithms are compiled and predicted by logistic regression, and the stacking ensemble classifier that is applied produces better accuracy than the stand-alone classifier, which is 75.3 percent
Aplikasi Parkir Berlangganan Untuk Karyawan Tenant pada Mal Summarecon Bekasi Rineke Meisyha; Henny Leidiyana
Bianglala Informatika Vol 11, No 2 (2023): Bianglala Informatika 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/bi.v11i2.16889

Abstract

Abstrak  - Bertambahnya jumlah penduduk dan perkembangan ekonomi masyarakat, saat ini banyak yang memiliki kendaran sendiri. Di kota yang semakin padat, menemukan tempat parkir yang nyaman  bisa menjadi sulit, misalkan pada sebuah mal. Belum lagi pada akhir pekan atau libur menimbulkan antrian yang salah satu penyebabnya yaitu saat pembayaran. Pengguna fasilitas parkir bukan hanya pengunjung tetapi karyawan mal. Pada mal Sumarecon Bekasi, karyawan juga membayar parkir berlangganan dan mereka harus melaporkan transaksi perpanjangan parkir bulanan mereka. Biasanya hal tersebut dilakukan disela jam istrirahat atau waktu lainnya. Karena keterbatasan waktu maka penelitian ini membahas tentang perancangan aplikasi parkir berlangganan parkir online pada mal Sumarecon Bekasi. Aplikasi ini dirancang dengan metode Air Terjun, menggunakan UML dan ERD sebagai metode perancangan system dan basis data. Pengujian antarmuka dilakukan  untuk calon pengguna prototype. Pengujian antarmuka ini dilakukan menggunakan 2 macam pengujian, yaitu pengujian Front-end dan pengujian Back-end. Dengan adanya aplikasi ini dapat memberikan solusi bagi para karyawan mal agar lebih hemat dan praktis dalam melaporkan transaksi pembayaran berlangganan parkir yang selama ini dilakukan secara manual.Kata kunci: Mal Summarecon, Parkir Berlangganan, Metode Air Terjun Abstract  - With the increasing population and economic development of the community, many now have their own vehicles. In an increasingly crowded city, finding a convenient parking space can be difficult, say in a mall. Not to mention that on weekends or holidays it creates queues, one of the reasons for this is the time of payment. Users of parking facilities are not only visitors but mall employees. At Summarecon Bekasi mall, employees also pay for subscription parking and they must report their monthly parking renewal transactions. Usually this is done during breaks or other times. Due to time constraints, this research discusses the design of an online parking subscription application at the Summarecon Bekasi mall. This application is designed using the Waterfall method, using UML and ERD as a system and database design method. Interface testing is carried out for prospective prototype users. Interface testing is carried out using 2 types of testing, namely Front-end testing and Back-end testing. With this application, it can provide a solution for mall employees to be more economical and practical in reporting parking subscription payment transactions which have been done manually.Keywords: Summarecon Mall, subscription parking, Waterfall method
SISTEM INFORMASI PENGGAJIAN KARYAWAN BERBASIS WEB MENGGUNAKAN FRAMEWORK LARAVEL Gustina, Rina; Leidiyana, Henny
Jurnal Sistem Informasi Vol 7 No 1 (2020)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (377.946 KB) | DOI: 10.30656/jsii.v7i1.1726

Abstract

Abstrak - PT. Evershine Convertindo merupakan perusahaan yang bergerak dalam bidang Industri Kertas. Sistem yang ada hanya berupa berkas sederhana. Kelemahan dari sistem tersebut tidak efisien, banyak  memakan waktu dalam pencatatan dan mengolah proses penggajian. Proses pencatatan dan perhitungan gaji yang diterapkan oleh perusahaan masih bersifat manual sehingga menyebabkan proses gaji sering terlambat. Dalam menyelesaikan masalah tersebut maka penulis merancang suatu sitem informasi penggajian karyawan. Dalam perancangan ini, penulis menggunakan bahasa pemrograman PHP dengan format database MySql, menggunakan Metode Waterfall dan menggunakan framework Laravel. Dengan rancangan sistem  tersebut diharapkan perusahaan akan memperoleh beberapa kemudahan dalam menginput data sekaligus membantu pihak perusahaan untuk menyusun laporan penggajian menjadi lebih cepat dan lebih efisien. Kata kunci: Penggajian Karyawan, PHP, MySql, Waterfall, Laravel
MENGUKUR KEPUASAN MAHASISWA DALAM MENGGUNAKAN APLIKASI MUSIC STREAMING MENGGUNAKAN METODE AHP Rakhmah, Syifa Nur; Leidiyana, Henny
INTI Nusa Mandiri Vol 19 No 1 (2024): INTI Periode Agustus 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i1.5577

Abstract

Online streaming applications are currently very popular among the public, especially students. Because of this user interest, the author wanted to conduct research. This research uses the AHP method to measure students' level of satisfaction with the use of music streaming applications. The research evaluation criteria included quality, service, price and payment. Questionnaires are used to determine student preferences and assessment of related criteria. The collected data was analyzed using the AHP method and the application priorities were compared. These findings will help developers and users improve quality and user experience. The research was conducted using the Analytical Hierarchy Process (AHP) methodology on students in the Bekasi City area with a population of 7058 people and obtained a sample size of 379 respondents using the Slovin formula. The research results show that Spotify is the most popular music streaming application among users, especially students. Followed by applications such as Joox and YouTube.
Model Klasifikasi Risiko Stunting Pada Balita Menggunakan Algoritma CatBoost Classifier Pahlevi, Omar; Wulandari, Dewi Ayu Nur; Rahayu , Luci Kanti; Leidiyana, Henny; Handrianto, Yopi
Bulletin of Computer Science Research Vol. 4 No. 6 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i6.373

Abstract

Stunting is a significant health issue in Indonesia, affecting the growth and development of young children and influenced by various complex risk factors such as nutrition, environment, and access to healthcare services. The manual process of identifying stunting risks often requires considerable time, resources, and specialized expertise from medical professionals. This study aims to develop a stunting risk classification model for young children using machine learning through the CatBoost Classifier algorithm. This algorithm was chosen for its advantages in handling categorical variables without requiring complex encoding processes and its ability to manage imbalanced data, ultimately improving prediction accuracy. In the conducted case study, the model's prediction updates were illustrated by increasing the initial prediction from 0.25 to 0.27 after accounting for residual corrections in the first iteration, with a learning rate of 0.1. This process demonstrates CatBoost's iterative mechanism for improving model predictions through gradual updates. Evaluation results showed that the developed model achieved an accuracy of 98.47% and a ROC-AUC score of 1.00 for several classes, indicating a high capability in accurately classifying stunting risks. These findings suggest that the CatBoost algorithm is effective for stunting risk classification, capable of handling data complexity, and expected to contribute significantly to supporting stunting prevention efforts through improved early detection.
Prediksi Limit Kredit Menggunakan Metode Regresi Linear Yusuf, Ahmad; Leidiyana, Henny; Budiawan, Imam
Socius: Jurnal Penelitian Ilmu-Ilmu Sosial Vol 3, No 2 (2025): September
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.17212143

Abstract

Determining appropriate credit limits is essential for financial institutions to manage credit risk effectively while optimizing revenue. This study aims to develop a predictive model for credit limits using linear regression, incorporating primary features such as Rating, Income, and Balance. The dataset consists of 400 credit card customer records with 11 variables, comprising both numerical and categorical data. The research follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, covering stages including business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Data analysis was conducted using Google Colab, involving quality assessment, categorical feature encoding through label encoding, and data normalization utilizing MinMaxScaler. Correlation analysis results indicated that Rating, Income, and Balance have strong correlations with Credit Limit, hence these three variables were chosen as primary predictors for the modeling process. 
Co-Authors Agustiena Merdekawati, Agustiena Ahmad Yusuf Al Ghazali Ali Rachman, Ali Andrie Kurniawan Angga Aditya Permana Anugrah, Arya Deval Saladin Ridwan Dewi Ayu Nur Wulandari Fajar Santoso Farida Cinda R. S Simatupang Gustina, Rina Hariyanto, Risvan Dwi Hary Priantoro Imam Budiawan Iqbal Fadjarudin Luci Kanti Rahayu Muhammad Adil Muhammad Faisal Muhammad Ghudafa Taufiq Akbar Nandang Iriadi Nila Hardi Omar Pahlevi Purnamawati Purnamawati Rineke Meisyha Rio Sudarsono Simanungkalit, Jumantho Simanungkalit, Jumantho Syifa Nur Rakhmah, Syifa Nur Waryono Yopi Handrianto, Yopi