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All Journal Jurnal Ilmiah Informatika Komputer Jurnal ULTIMA Computing Journal of Soft Computing Exploration Journal of Artificial Intelligence and Digital Business
Sintawati, Andini
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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APLIKASI SISTEM INFORMASI PERUSAHAAN OTOBUS PARIWISATA DI DKI JAKARTA MENGGUNAKAN TEKNOLOGI ANDROID MBAAS Hidayat, Ahmad; Sintawati, Andini
Jurnal Ilmiah Informatika Komputer Vol 24, No 1 (2019)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2019.v24i1.1990

Abstract

Kebutuhan masyarakat untuk mencari informasi dan pemesanan kendaraan wisata yang mudah, cepat dan dapat diakses dimana saja masihlah sulit. Jika pemesanan kendaraan wisata yang diinginkan harus melalui orang ketiga atau calo dinilai sangat merugikan untuk masyarakat ataupun untuk pihak PO bus karena harga yang ditawarkan oleh calo biasanya lebih mahal dari harga asli yang ditawarkan PO bus. Tujuan dari penelitian ini adalah membuat suatu aplikasi yang dapat membantu masyarakat untuk mencari informasi dan pemesanan jenis kendaraan pariwisata kepada pihak PO bus pariwisata yang ingin digunakan berdasarkan kebutuhan langsung dari smartphone yang mereka miliki. Aplikasi   ini  dibuat  dengan  menggunakan  perangkat lunak Framework Jquery Mobile, Android Project, SDK Apiomat, Notepad++, browser Mozilla Firefox, dan Adobe PhoneGap. Metode  yang  digunakan  adalah pendekatan  Software  Development  Life  Cycle (SDLC), yang  terdiri  dari  fase perencanaan, analisa, perancangan, implementasi dan uji coba. Berdasarkan uji coba, aplikasi ini dapat diakses secara langsung melalui smartphone android dengan teknologi mobile backend as a service (MBaaS) yang terkoneksi internet, dan informasi yang diberikan aplikasi ini adalah mengenai kumpulan nama PO bus, alamat pusat, alamat cabang, fasilitas kendaraan, harga, peta lokasi, kontak telepon dan email yang dimiliki PO bus sesuai informasi yang dibutuhkan masyarakat khususnya yang tinggal di DKI Jakarta.
An Automatic Internet of Things-Based System for Rabbit Cage Kharisma, Andrian; Sintawati, Andini
Ultima Computing : Jurnal Sistem Komputer Vol 15 No 2 (2023): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v15i2.3404

Abstract

Rabbits, low-maintenance mammals in terms of cost and space requirements, require meticulous care, encompassing disease control, feeding, and cage maintenance. To address these concerns, an automated system for feeding, drinking, temperature control, and monitoring rabbit manure gas levels within the cage was developed, all remotely accessible. The system comprises ultrasonic sensors, DHT11 sensors, MQ-135 gas sensors, a real-time clock (RTC), an Arduino Mega 2560 with built-in Wi-Fi, relays, servo motors, mini water pumps, mini fans, and a heat lamp. The feeding and drinking functions are automated, triggered by RTC sensor data or can be manually controlled through the Arduino IoT Cloud dashboard. Temperature regulation is managed based on data from the DHT11 sensor, and gas levels in the rabbit manure are monitored using the MQ-135 gas sensor. Conducting 30 tests for each primary function, including automatic and manual feeding/drinking, temperature control, and disinfectant spraying, these functions performed as designed. An exception occurred three times when the DHT11 microcontroller sensors lost connection, rendering the input from these sensors unusable. To address this issue, the addition of an extra voltage supply to the Arduino Mega 2560 microcontroller is proposed, mitigating this vulnerability.
Flood early warning system at Jakarta dam using internet of things (IoT)-based real-time fishbone method to support industrial revolution 4.0 Farabi, Muhammad Rizqi Al; Sintawati, Andini
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.293

Abstract

This research aims to develop an effective flood early warning system to provide timely information to the public and support the government in disaster management. The Raspberry Pi mini-computer functions as the central control, collecting data from the Water Level Sensor to measure water height, the Ultrasonic Sensor for further monitoring, the DHT11 Sensor to monitor temperature and humidity, and a Buzzer as an audible warning device. The research method involves review of the literature and data acquisition from related journals. These data are utilized to design an Internet of Things (IoT)-based flood detection tool with the Raspberry Pi minicomputer as the main controller. The system can be implemented in vulnerable locations such as reservoirs, sluice gates, and rivers, as part of the Smart City and Smart Environment concepts. The test results indicate that the developed early warning system, integrating the Raspberry Pi minicomputer, the Water Level Sensor, the the Ultrasonic Sensor DHT11 Sensor, and Buzzer, approaches perfection. Real-time information is transmitted through the Twitter social media platform, which is shown to be more effective than manual notifications. The system can provide accurate early warnings, reduce flood-related damages, and positively contribute to flood prevention and disaster management efforts. This research is expected to make a significant contribution to improving the community and government preparedness for future flood disasters.
Evaluasi Kinerja SVM dan Logistic Regression pada Data Multiclass dalam Analisis Sentimen Film Dirty Vote dengan Metode Pelabelan Lexicon Based Sintawati, Andini; Amalya, Farida; Hidayat, Ahmad; Supyan, Nur Muhammad
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.576

Abstract

Pada 11 Februari 2024, saluran Youtube Dirty Vote dan PSHK Indonesia merilis film berjudul Dirty Vote yang menuai perdebatan masyarakat. Dalam rangka mengetahui sentimen masyarakat, dilakukan analisis sentimen. Analisis sentimen merupakan metode untuk mengkategorikan sentimen dengan melibatkan Natural Language Processing (NLP) dan algoritma machine learning, seperti Support Vector Machine (SVM) dan Logistic Regression. Penelitian sebelumnya telah membandingkan kedua algoritma tersebut dalam melakukan analisis sentimen pada dua atau tiga kategori. Namun, pada penelitian tiga kategori SVM hanya dilatih dan diuji dengan kernel RBF. Oleh karena itu, dilakukan penelitian untuk membandingkan nilai akurasi model SVM dan Logistic Regression dalam mengklasifikasikan sentimen film Dirty Vote. SVM dilatih dan diuji menggunakan tiga kernel, yaitu Polynomial, RBF, dan Sigmoid. Penelitian ini menggunakan tahapan-tahapan NLP dengan menggunakan data sebanyak 3.500 yang berasal dari proses scraping film Dirty Vote. Data digolongkan menjadi tiga kategori, yaitu sentimen negatif sebesar 36,31%, sentimen positif sebesar 31,95%, dan sentimen netral sebesar 31,74%. Data tersebut dibagi menjadi data latih dan data uji dengan rasio 90:10, 80:20, 70:30. Dari penelitian ini, diperoleh hasil rata-rata akurasi untuk ketiga kernel SVM dan Logistic Regression. Hasil penelitian menunjukan nilai akurasi tertinggi diperoleh oleh algoritma SVM dengan kernel RBF sebesar 77%, diikuti oleh Logistic Regression sebesar 76%, kernel Sigmoid sebesar 75%, dan kernel Polynomial sebesar 65%.
Co-Authors Ahmad Hidayat Amalya, Farida Farabi, Muhammad Rizqi Al Kharisma, Andrian Supyan, Nur Muhammad