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All Journal PROCEEDING IC-ITECHS 2014 JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal Informatika Universitas Pamulang Applied Information System and Management JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Teknologi dan Sistem Tertanam Journal Social Science And Technology For Community Service Journal of Soft Computing Exploration Jurnal Pendidikan dan Teknologi Indonesia Jurnal Telematics and Information Technology (TELEFORTECH) Jurnal Locus Penelitian dan Pengabdian Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Informatika: Jurnal Pengembangan IT
Styawati, Styawati
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Aerospace Engineering Automotive Engineering 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 Languange, Linguistic, Communication & Media Mathematics

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Rancang Bangun Sistem Monitoring Inkubator Telur Otomatis Berbasis Iot Menggunakan Sensor Dht22 Dan Mikrokontroler Esp-32 Rahayu, Usman Puji; Styawati, Styawati
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 7 (2025): JPTI - Juli 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.907

Abstract

Peternakan merupakan salah satu sektor utama penyedia pangan setelah pertanian, salah satunya melalui usaha peternakan bebek. Untuk meningkatkan produksi, peternak umumnya melakukan perkembangbiakan dengan metode penetasan buatan menggunakan alat penetas telur. Alat ini memiliki peran penting dalam menghasilkan anakan bebek berkualitas. Faktor-faktor seperti suhu, kelembaban, dan rotasi telur harus dikendalikan dengan baik agar proses penetasan berlangsung optimal, sehingga diperlukan pemantauan dan pengawasan kondisi inkubator secara berkala. Penerapan teknologi Internet of Things (IoT) dalam bidang peternakan menjadi solusi untuk melakukan pemantauan kondisi inkubator secara otomatis dan real-time. Teknologi ini membantu peternak dalam memonitor suhu, kelembaban, dan kondisi telur di dalam ruang penetasan secara efisien. Penelitian ini mengusung judul "Sistem Monitoring Suhu dan Kondisi Inkubator Telur Menggunakan Sensor DHT22 dan ESP-32 CAM" yang bertujuan untuk memudahkan peternak dalam melakukan pemantauan ruang penetasan. Berdasarkan hasil pengujian, sistem yang dikembangkan mampu melakukan monitoring suhu, kelembaban, dan kondisi telur secara otomatis dan real-time, serta mengendalikan pemutaran telur secara otomatis guna meningkatkan tingkat keberhasilan penetasan.
Sentiment Analysis of COVID-19 Booster Vaccines on Twitter Using Multi-Class Support Vector Machine Nurkholis, Andi; Styawati, Styawati; Alim, Syahirul; Saputra, Hendi; Ferriyan, Andrey
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.42911

Abstract

The Indonesian government's implementation of a booster vaccination program as part of its COVID-19 response has generated diverse public reactions, particularly on social media platforms like Twitter. This study aims to analyze public sentiment regarding booster vaccines by examining Twitter data to understand the prevailing discourse and attitudes toward this policy. The research employs sentiment analysis, a text mining and processing technique, to classify tweets into positive, neutral, and negative categories. The study utilizes the Support Vector Machine (SVM) algorithm, evaluating its performance through a multi-class parameter assessment. Two multi-class strategies, One-against-one (OAO) and One-against-rest (OAR) are combined with various kernels (Sigmoid, Polynomial, and RBF) to identify the most accurate model for sentiment classification. The results show that the OAO method with the RBF kernel achieves the highest accuracy of 96%, outperforming other combinations like OAO with Polynomial (95.2%) and Sigmoid (93.7%) kernels. Similarly, the RBF kernel performs best with 95.5% accuracy in the OAR approach. Using the optimal model, sentiment analysis classifies 49 tweets as positive, 927 as neutral, and 24 as negative, revealing a predominantly neutral public sentiment with limited positive and negative opinions. In conclusion, this study demonstrates the effectiveness of SVM, particularly the OAO method with the RBF kernel, for sentiment analysis of social media data. The findings provide insights into public perceptions of the booster vaccine program, offering policymakers a data-driven basis for designing targeted communication strategies to address concerns and enhance public acceptance.
Pengembangan Sistem Pengukur Curah Hujan Otomatis Berbasis Iot dan Monitoring Suhu Lingkungan Menggunakan Sensor SHT31 Edistira, Khusnul; Samsugi, S.; Styawati, Styawati
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 10 (2025): : JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i10.4983

Abstract

Perubahan iklim global telah meningkatkan variabilitas curah hujan, sehingga memerlukan sistem monitoring yang akurat dan real-time untuk mendukung sektor pertanian dan hidrologi. Penelitian ini bertujuan untuk mengembangkan sistem pengukur curah hujan otomatis dan monitoring suhu lingkungan berbasis Internet of Things (IoT) yang mampu memberikan data akurat dan real-time. Sistem ini menggunakan sensor Load Cell untuk mengukur berat air hujan yang dikonversi menjadi satuan milimeter, serta sensor SHT31 untuk mendeteksi suhu dan kelembapan dengan tingkat presisi tinggi. Data hasil pengukuran dikirim secara otomatis melalui mikrokontroler ESP32 ke server dan ditampilkan pada antarmuka web. Metode penelitian menggunakan pendekatan prototype dengan tahapan communication, quick plan, modeling quick design, construction of prototype, serta deployment and feedback. Berdasarkan hasil pengujian, perbedaan antara data pengukuran manual menggunakan gelas ukur dan sistem otomatis berbasis sensor sangat kecil, dengan selisih rata-rata hanya 10% dan tingkat akurasi mencapai 99,8%. Pada parameter suhu dan kelembapan, sensor SHT31 menunjukkan selisih rata-rata sebesar 0,317°C dan 2,04% dibanding alat ukur digital, dengan akurasi keseluruhan di atas 97%. Hasil ini menunjukkan bahwa sistem yang dikembangkan mampu menghasilkan data yang stabil, akurat, dan konsisten, sehingga dapat diandalkan sebagai solusi modern untuk pemantauan cuaca berbasis data digital yang mendukung analisis hidrologi dan sektor pertanian. Sistem ini memberikan kontribusi signifikan dalam penyediaan data cuaca real-time yang dapat diakses secara luas melalui platform web, sehingga mendukung pengambilan keputusan berbasis data dalam manajemen sumber daya air dan pertanian.
Prediction Model for Soybean Land Suitability Using C5.0 Algorithm Nurkholis, Andi; Styawati, Styawati
JOIN (Jurnal Online Informatika) Vol 6 No 2 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i2.711

Abstract

Soybean is one of the protein main sources that can be used for consumption in tempeh, tofu, milk, etc. Based on projection results, soybean production and consumption balance in Indonesia, in 2018-2022, it is estimated that deficit will increase by 6.18% per year. So, it's necessary to guide soybean land suitability, which can be carried out by evaluating existing land suitability to support soybean farming expansion and production. This study conducted an analytical study to evaluate soybean land suitability using C5.0 algorithm based on land and weather characteristics. The C5.0 algorithm is an extension of spatial decision tree, an ID3 decision tree extension. Dataset is divided into two categories: explanatory factors representing seven land characteristics (drainage, land slope, base saturation, cation exchange capacity, soil texture, soil pH, and soil mineral depth) and two weather data (rainfall and temperature), and a target class represent soybean land suitability in two study areas, namely Bogor and Grobogan Regency. The result generated two land suitability models with the best model obtained accuracy for training data 98.58%, while testing data was 97.17%. The best model rules are 69 rules that do not involve three attributes: cation exchange capacity, soil mineral depth, and rainfall.
Sentiment Analysis of Cyber Attacks in Bank Syariah Indonesia Using SVM and Indobert Method Apriyadi, Chandra; Styawati, Styawati
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.2636

Abstract

Bank Syariah Indonesia (BSI) is one of the Islamic banking institutions that operates based on Islamic principles in accordance with Islamic law and has obtained an operational license from the Dewan Syariah Nasional (DSN). The advancement of information technology brings unique risks to the banking industry, including BSI. One example is the ransomware attack experienced by BSI from May 8 to 11, 2023, where 15 million customer data and 1.5 terabytes of internal data were stolen, leading to significant public concern and response across various media platforms. This has the potential to affect public trust in the Islamic banking industry, particularly BSI. This research aims to analyze public sentiment on Twitter regarding the attack to identify the majority sentiment formed, as well as to compare the performance of the SVM and IndoBERT models in classifying sentiments. Additionally, this study reveals the topics present in the negative sentiments based on the classifications of both models through topic modeling using Latent Dirichlet Allocation (LDA). The results indicate that the majority of sentiments are negative, while IndoBERT shows better performance compared to SVM, with an accuracy of 85% and an F1-Score of 82%. The topics present in the negative sentiments classified by SVM include issues related to fund security as well as transfers and withdrawals, whereas the topics present in the negative sentiments classified by IndoBERT are more related to problems with mobile banking and fund withdrawals.
Implementasi Teknologi Berbasis Web untuk Efesiensi Waktu Pencarian Lahan Parkir: Implementation of Web-Based Technology for Efficient Time to Search for Parking Spaces Yudha, Sandy; Rahmanto, Yuri; Styawati, Styawati
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 2 (2024): MALCOM April 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i2.1269

Abstract

Penelitian ini bertujuan untuk mengimplementasikan teknologi berbasis web guna meningkatkan efisiensi waktu pencarian lahan parkir di kota-kota metropolitan. Sistem yang dikembangkan diharapkan dapat memberikan informasi real-time tentang ketersediaan lahan parkir, memandu pengguna menuju tempat parkir yang sesuai, dan mengurangi waktu pencarian secara signifikan. Metode penelitian ini melibatkan perancangan sistem berbasis web, dan pembuatan prototipe. Data ketersediaan lahan parkir akan dikumpulkan melalui sensor IR. Dan akan dikirimkan  dari Mikrokontroler ESP 8266 Ke Website parkir. Implementasi teknologi berbasis web untuk efisiensi waktu pencarian lahan parkir diharapkan dapat memberikan hasil positif. Pengguna akan dapat mengakses informasi real-time tentang ketersediaan lahan parkir, mengurangi waktu pencarian, dan menghindari kepadatan lalu lintas yang tidak perlu. Selain itu, penerapan teknologi ini diharapkan dapat meningkatkan pengelolaan lahan parkir secara keseluruhan dan memberikan efek  positif terhadap keberlanjutan lingkungan. Dengan menggabungkan teknologi berbasis web dan sensor IR, sistem ini dapat menjadi solusi efektif untuk meningkatkan efisiensi waktu pencarian lahan parkir yang semula tanpa website parkir memakan waktu 29 detik menjadi 16 detik saja sehingga dapat meminimalisir waktu sebanyak 13 detik. Implikasi positif dari penelitian ini diharapkan dapat memberikan kontribusi terhadap kemajuan kota-kota modern menuju sistem transportasi yang lebih efisien dan berkelanjutan.
Firefly Algorithm for SVM Multi-class Optimization on Soybean Land Suitability Analysis Nurkholis, Andi; Styawati, Styawati; Suhartanto, Alvi
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1860

Abstract

Soybean is the primary source of vegetable protein nutrition, containing fat and vitamins that Indonesian people widely consume. The decline in soybean production in Indonesia every year is due to the reduced area of soybean cultivation, thereby increasing dependence on imports from other countries. Land suitability maps can provide directions for priority locations for soybean cultivation based on land characteristics and weather to produce optimal production. The SVM multi-class algorithm has been applied to classify land suitability data to create a land suitability map but has yet to obtain optimal accuracy, especially for sigmoid kernels. The objective of this study is to enhance the performance of the sigmoid kernel SVM by utilizing the firefly algorithm. The study focuses on evaluating the suitability of soybean cultivation in Bogor and Grobogan Regencies. The results of the tests indicate that the firefly algorithm-optimized SVM (FA-SVM) significantly improves accuracy compared to the SVM without optimization. The accuracy achieved by FA-SVM is 89.95%, while the SVM without optimization only achieves an accuracy of 65.99%. The best parameters produced by the firefly algorithm are C=2.33 and σ=0.45 obtained from firefly customization, and the number of generations is 10. Based on this, the optimization algorithm can be used to produce an optimal model. The best optimal model obtained can be used as a guide for priority locations/areas for soybean cultivation by farming communities, so as to produce maximum soybean productivity.
Implementasi Sistem IoT Pada Akuakultur Dan Hydroponik (Akuaponik) Modern Untuk Pertumbuhan Ikan Nila Setiawan, Bagas; Styawati, Styawati; Alim, Syahirul
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 1 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i1.5896

Abstract

In modern times, there are many types of agricultural and fish farming systems, one of which is the modern aquaculture system which has been recognized as an innovative method of sustainable food production that combines fish cultivation with agriculture simultaneously. Modern aquaponics is able to overcome problems in urban areas which require land for growing crops and cultivating fish. Good fish cultivation means always monitoring the growth and health of fish to reduce the risk of crop failure, therefore this research aims to implement an Internet of Things (IoT) system in modern aquaponics to increase the growth of tilapia. IoT parameters consisting of pH sensors, Total Dissolved Solids (TDS) sensors, and temperature sensors are used to monitor water quality conditions in modern aquaponics. Through IoT systems, data collected in real-time enables more effective environmental monitoring. The creation of an IoT system in modern aquaponics shows that the implementation of IoT can increase the efficiency of modern aquaponic environmental management, resulting in better fish growth and plant productivity. This was verified through the pH sensor test results with a value of 7.2 indicating optimal conditions of acid-base balance in the water, which is essential for the health of tilapia fish. The TDS sensor test with a value of 300 ppm confirmed that the concentration of solid particles in the water was at an optimal level, which is also an indicator of the health of the tilapia fish. Temperature measurement using a temperature sensor with a value of 28°C, shows that the water temperature is within the ideal range for comfort for tilapia, which is usually comfortable at temperatures between 28-30°C
Analisis Sentimen Masyarakat Indonesia Terkait Vaksin Covid-19 Pada Media Sosial Twitter Menggunakan Metode Support Vector Machine (Svm) Arfat, Muhammad Fadilah; Styawati, Styawati; Nurkholis, Andi; Kurniawan, Indra
Jurnal Informatika: Jurnal Pengembangan IT Vol 7, No 2 (2022)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v7i2.3549

Abstract

COVID-19 is a new disease outbreak that has been officially designated as a global pandemic by the Worldi Health Organizationi (WHO) oni March 11, 2020. Seeing the rapid development of COVID-19, the Government of Indonesia has carried out vaccinations that have been carried out since January 13, 2021, this vaccination is prioritized for medical personnel and red zone areas. Since its emergence, therei have been many prosi andi consi regardingi the vaccination process and it has alsoi become a trending topici on sociali media Twitter oni January 13, 2021. Onei of the mosti widely used social media by Indonesiani people isi twitter sociali media. According to We arei Social sources in 2020, twitteri social media is rankedi fifth in the category of sociali media that is often used with a user percentage of 56% after Youtube, Whatsapp, Facebook as well as Instagram. Thisi shows that there is a huge opportunity for data sources that can be usedi to find out the positive and negativei sentiments of the related community, which is useful for interested parties to carry out evaluations. So that it can see how many people agree and disagree. If the percentage of people who disagree is more, the government must do better socialization so that people can better understand and not feel afraid of the vaccine. This study aims to find out how public sentiment is about the government's policies regarding the COVID-19 vaccinei using the Support Vector Machine method. by extracting the tf-idf feature and comparing the kernels contained in the SVM, including Linear, RBF, Polynomial, and Sigmoid. With tests that will later see how the values of accuracy, precision, recall and F1-Score are. 
Teknologi Deteksi Dini Banjir Daerah Aliran Sungai menggunakan Heltec Wifi LoRa 32 V2 Amanda, Feby; Samsugi, Selamet; Styawati, Styawati; Alim, Syahirul
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 1 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i1.5892

Abstract

In Indonesia there are often natural disasters, one of which is flooding. Flooding is a natural disaster that is marked by the overflowing of river water irrigation channels in urban areas, one is the river Irrigation that exists at the Technokrat University of Indonesia. Therefore, the study aims to develop a flood early detection tool using LoRa (Long Range) technology to monitor potential flooding in Kalibalau, Indonesian Technocratic University, Bandar Lampung. The research method involves installing an ultrasonic sensor in the Kalibalau River and connecting it to the Heltec Wifi LoRa 32 V2 microcontroller. Test results show that the LoRa transmitter and receiver operate as planned. This tool does not require an internet connection because it uses the Heltec Wifi LoRa 32 V2. The status of the river is categorized into four: Safe, Alert 1, Alert 2, and Danger, with appropriate warnings. The test showed a delay of 5 seconds on the water height reading. At safe (water height 44 cm), the buzzer does not sound. At morning 1 (water altitude 82 cm), it sounds once with a 1 minute delay. The device has a communication capacity of up to 400 meters. Thus, the tool is effective in monitoring the Kalibalau river and giving early warning of potential floods. This research has contributed to the development of flood monitoring technology to increase public alertness and safety in flood-prone areas
Co-Authors Adrian, Qodhli Jafar Afredha, Arya Agustian, Rezi Dwi Al Faroqi, M. Ibnu Aldo Pramana Amanda, Feby Andi Nurkholis Andika, Rio Andrey Ferriyan, Andrey Anggita, Rizki Devi Apriyadi, Chandra Ardani, Ravi Ari Tri Arfat, Muhammad Fadilah Asyad, Chafidz Azahra, Maulida Bagas Setiawan, Bagas deviana, lyla putri Dewa, Ananda Asmoro Edistira, Khusnul Febriyansyah, Akhmad Febriyo Hepri, Alfian I Komang Arya Ganda Wiguna Ida Bagus Gede Sarasvananda Indra Kurniawan Indraningtyas, Lathifa Ismail, Izudin Jaya, Dicky Suman Jupriyadi, Jupriyadi Khoiri, Rizky Dafa Kurniawan, Medy Dwi Kuswara, Muhammad Shidiq Mahendra, Anton Pranata, Rangga Prastowo, Agung Tri Pratama, Ariski Qadhli Jafar Adrian Rahayu, Usman Puji Rifai, Jefri Andri Rizki Dalimunthe, Ernando S. Samsugi Saputra, Dian Tri Saputra, Hendi Satrya, Alan Suhartanto, Alvi Syahirul Alim Syahirul, Alim Wijaya, Rio Yudha, Sandy Yulistiani Yulistiani, Yulistiani Yuri Rahmanto