Garuda - Garba Rujukan Digital

PENINGKATAN KEAMANAN DATA END-TO-END SMART DOOR MENGGUNAKAN ADVANCED ENCRYPTION STANDARD Adhiwibowo, Whisnumurti; Hirzan, Alauddin Maulana; Suprayogi, Muhammad Sani
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 6 No. 2 (2022)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v6i2.574

Smart Home is one form of implementation of Internet of Things technology in the form of smart homes that can carry out management, monitoring, even reporting. In addition, smart homes can be equipped with security equipment such as Smart Door that can open or lock the door automatically when recognizing the homeowner's face. However, the current Smart Door model has a disadvantage where the stored data on the server and the device are not secured end-to-end. The homeowners' image data on the device is not encrypted with a specific algorithm and validation. Thus, the outside parties can use this high-risk problem to enter the house unnoticed. They disguised themselves as the homeowner by entering false data on the device. Based on this problem, this study has a purpose to increase the model's end-to-end security by implementing the Advanced Encryption Standard algorithm. In addition to increase the security level, the Truncated Decimal-converted SHA-1 checksum validation is added to prevent modifications in each image data. From the results of the model comparison experiment, there was an increase in device resource needs as much as 0.81% increase in process time; 18% CPU usage; 5.3% data usage; and 5.04% for the use of the entire process of memory. But the increase in performance needs is not comparable to the security features presented by the Advanced Encryption Standard algorithm in securing data and servers. So that with improvisation this security is expected to improve the data security of homeowners from outside parties.

Internet of things based seasonal auto regression integrated moving average model for hydroponic water quality prediction Daru, April Firman; Susanto, Susanto; Adhiwibowo, Whisnumurti; Hirzan, Alauddin Maulana
International Journal of Advances in Applied Sciences Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i1.pp123-131

Technological progress significantly impacts agriculture, with the rapid expansion of industrial and residential areas leading to a scarcity of agricultural land. Modern farming techniques like hydroponics have emerged as a solution, allowing plant growth with water as a medium. Real-time monitoring of water quality is crucial for hydroponic systems. Lettuce (Lactuca sativa) is particularly compatible with hydroponics due to its short growth cycle and nutritional value. Key factors for successful cultivation include maintaining pH, temperature, and nutrient levels within optimal ranges. To address water quality monitoring complexities, internet of things (IoT) technology offers a promising solution. IoT devices autonomously gather environmental metrics such as temperature, pH, humidity, and nutrient concentrations. This study integrates an IoT-driven hydroponic water quality monitoring system using the seasonal auto-regressive integrated moving average (SARIMA) algorithm and the ESP32 microcontroller. This approach allows real-time water quality management, enhancing lettuce cultivation efficiency and productivity. The proposed model achieved 98.6% accuracy, effectively predicting water quality.

Hybrid Filtering for Student Major Recommendation: A Comparative Study Nurtriana Hidayati; Titin Winarti; Alauddin Maulana Hirzan
Advance Sustainable Science Engineering and Technology Vol. 7 No. 1 (2025): November-January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i1.1250

Choosing the right university major is an important decision for students, as delays or incorrect choices can harm their future careers and cause problems for academic departments. High dropout rates, which are frequently the result of poorly informed decisions, can be a considerable burden on faculty. This project aims to address these challenges by creating a recommendation system that provides individualized counsel to students based on their psychological profiles. A quantitative method was used, with questionnaires distributed to a large number of students. To verify the data's authenticity, replies were sought from students who were pleased with their selected majors rather than those who regretted their choices. The collected data formed the basis for a hybrid recommendation system that integrated Content-based Filtering and Collaborative Filtering methods. The system was then compared against standalone implementations of each filtering method to determine its usefulness in increasing suggestion accuracy. The results showed that the Hybrid Filtering strategy obtained a recommendation accuracy of 84.29%, outperforming Content-based Filtering at 81.43% and Collaborative Filtering at 78.57%. The proposed model is easy to implement in a school or a university, as long as the required data is available. Thus, the model can help a school or university to reduce dropout rates and boost academic outcomes.

Peningkatan Kemampuan Pemanfaatan Canva Untuk Kreasi Digital Di SMK Walisongo Semarang Hirzan, Alauddin Maulana; Daru, April Firman; Hartanto, Agus
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 2 (2025): MEI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i2.2904

Pelatihan desain grafis berbasis Canva untuk siswa SMK Walisongo Semarang bertujuan untuk meningkatkan keterampilan desain grafis yang relevan dengan kebutuhan industri digital. Metode pelatihan yang digunakan merupakan kombinasi ceramah dan praktikum berbasis asistensi. Ceramah menyampaikan teori dasar desain grafis dan pengenalan platform Canva, sementara praktikum memberikan kesempatan bagi siswa untuk mengaplikasikan pengetahuan dalam proyek desain yang relevan. Hasil dari kuesioner pra dan pasca pelatihan menunjukkan adanya peningkatan yang signifikan dalam pemahaman peserta. Skor pada aspek teori sedikit menurun dari 94% menjadi 90%, namun pemahaman desain meningkat dari 78% menjadi 82%, dan pemahaman produksi file/output melonjak drastis dari 52% menjadi 94%. Peningkatan ini menunjukkan bahwa pelatihan berhasil meningkatkan keterampilan teknis dan pemahaman peserta, memungkinkan mereka untuk mengaplikasikan konsep desain secara efektif dan menghasilkan output yang siap digunakan. Evaluasi ini juga mengindikasikan bahwa metode pelatihan berbasis praktikum dan asistensi sangat efektif dalam meningkatkan kepercayaan diri dan keterampilan peserta, yang sangat relevan dengan tuntutan dunia kerja.

Arowana cultivation water quality forecasting with multivariate fuzzy timeseries and internet of things Hirzan, Alauddin Maulana; Daru, April Firman; Huizen, Lenny Margaretta
Computer Science and Information Technologies Vol 6, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i2.p136-146

Water quality plays a crucial role in the growth and survival of arowana fish, with imbalances in key parameters (pH, temperature, turbidity, dissolved oxygen, and conductivity) leading to increased mortality rates. While previous studies have introduced various monitoring models using Arduino IDE and intrinsic approaches, they lack predictive capabilities, leaving cultivators unable to take proactive measures. To address this gap, this study develops a predictive model integrating the internet of things (IoT) with a fuzzy time series (FTS) algorithm. Through rigorous evaluation and validation, the proposed FTS-multivariate T2 model demonstrated superior performance, achieving an exceptionally low error rate of 0.01704%, outperforming decision tree (0.13410%), FTS-multivariate T1 (0.88397%), and linear regression (20.91791%). These findings confirm that FTS-multivariate T2 not only accurately predicts water quality but also significantly reduces the mean absolute percentage error, providing a robust solution for sustainable arowana aquaculture.

Hybrid Filtering for Student Major Recommendation: A Comparative Study Hidayati, Nurtriana; Winarti, Titin; Hirzan, Alauddin Maulana
Advance Sustainable Science Engineering and Technology Vol. 7 No. 1 (2025): November-January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i1.1250

Choosing the right university major is an important decision for students, as delays or incorrect choices can harm their future careers and cause problems for academic departments. High dropout rates, which are frequently the result of poorly informed decisions, can be a considerable burden on faculty. This project aims to address these challenges by creating a recommendation system that provides individualized counsel to students based on their psychological profiles. A quantitative method was used, with questionnaires distributed to a large number of students. To verify the data's authenticity, replies were sought from students who were pleased with their selected majors rather than those who regretted their choices. The collected data formed the basis for a hybrid recommendation system that integrated Content-based Filtering and Collaborative Filtering methods. The system was then compared against standalone implementations of each filtering method to determine its usefulness in increasing suggestion accuracy. The results showed that the Hybrid Filtering strategy obtained a recommendation accuracy of 84.29%, outperforming Content-based Filtering at 81.43% and Collaborative Filtering at 78.57%. The proposed model is easy to implement in a school or a university, as long as the required data is available. Thus, the model can help a school or university to reduce dropout rates and boost academic outcomes.

Temperature and Humidity Monitoring Using DHT22 Sensor and Cayenne API Adhiwibowo, Whisnumurti; Daru, April Firman; Hirzan, Alauddin Maulana
Jurnal Transformatika Vol. 17 No. 2 (2020): January 2020
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v17i2.1820

Internet of Thing is a new technology which enables small processing-capable devices to capture or retrieve information from a sensor and send the data to the central computer. This technology is useful for any sector especially agriculture, IoT can be used as monitoring or/and controlling the condition inside the cultivation. There are many kinds of agriculture cultivations, some of them have strict conditions due to the growth requirements. Oyster mushroom cultivation is one of many cultivations which requires strict temperature and humidity needed by oyster mushroom to growth optimal. The temperature inside the cultivation must be within 25 °C until 30 °C and humidity within 70% RH until 90% RH. Due to these strict requirements, the farmers required to check their cultivations every day and manually maintain the temperature and humidity, and this situation becomes a problem when the farmers own many cultivations. Hence, this research has a purpose to design an automatic monitoring system based on Internet of Things technology which utilizing the DHT22 sensor and Cayenne API as information retrieval medium to the computer. When using this device, the farmers can check their cultivation in the meantime without going inside the cultivation. This device will connect to the internet through wireless connectivity and send the captured information by the sensor to the farmers. The stored data from the cultivation can be exported as a CSV format if the farmers want to check the temperature and humidity statistic. The retrieved information from the sensor also displayed on the LCD attached to the device to ease the information reading near the cultivation.

Voice Over Internet Protocol Performance Evaluation in 6to4 Tunneling Network Hirzan, Alauddin Maulana; Bahaman, Nazrulazhar; Adhiwibowo, Whisnumurti
Jurnal Transformatika Vol. 18 No. 1 (2020): July 2020
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v18i1.2356

Registry reported that their regional already in exhausted state. The IPv6 was proposed to substitute IPv4 network, but the implementation of this version cased many problems such as hardware compatibility. As temporary solution to this problem, 6to4 tunneling transition mechanism is introduced as one of many solutions. This mechanism used IPv4 network as communication media between two IPv6 networks. Thus, this kind of mechanism will affect the performance of Voice over Internet Protocol. VoIP demanded real-time communication by using UDP protocol between nodes. Unlike normal communication mode, real-time mode required data to be sent immediately ignoring the quality of data. This research evaluated the performance of 6to4 tunneling mechanism for Voice over Internet Protocol s communication between two nodes in native IPv6 networks.

PEMANTAUAN TINGKAT KARBON MONOKSIDA DENGAN SENSOR MQ-9 STUDI KASUS UNIVERSITAS SEMARANG Hirzan, Alauddin Maulana; Adhiwibowo, Whisnumurti; Daru, April Firman
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol. 15 No. 2 (2024): September
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v15i2.741

Emisi merupakan hal yang sangat serius untuk di atasi, emisi gas karbon monoksida merupakan sisa dari kombusi yang dilakukan oleh kendaraan bermotor seperti motor maupun mobil. Gas ini bersifat tidak berbau maupun berwarna, namun mematikan dalam dosis yang tinggi. Sehingga perlu penanganan serius untuk memantau gas jenis ini. Universitas Semarang merupakan salah satu universitas dengan jumlah mahasiswa terbanyak di Jawa Tengah, maka secara otomatis pengendara kendaraan bermotor pun ikut meningkatkan kandungan gas karbon monoksida di area parkir. Oleh karena itu, penelitian ini memiliki tujuan untuk mendesain sebuah purwarupa deteksi karbon monoksida dengan menggunakan teknologi Internet of Things yang dapat melaporkan ketika terdapat kandungan gas di udara. Model ini dilengkapi dengan sensor MQ-9 yang mampu mendeteksi gas karbon monoksida lebih akurat dibandingkan model-model sebelumnya. Berdasarkan hasil evaluasi yang dilakukan, model ini mampu mendeteksi gas dengan rata-rata 4,17 ppm dari 55 data deteksi yang ada dan tersimpan di Firebase Realtime Database. Dari semua data yang ada, model mendeteksi puncak tertinggi mencapai 18,365 ppm. Meskipun terdapat kenaikan kandungan gas, namun hasil ini masih di bawah batas aman yang dianjurkan. Selain itu, hasil terakhir yang didapatkan dari model ini adalah notifikasi pelaporan yang disampaikan melalui Telegram Bot.

PEMANTAUAN KEBOCORAN GAS DAN PANAS UDARA DENGAN METODE FUZZY BERBASISKAN IOT Wicaksana, Dinar Anggit; Hirzan, Alauddin Maulana
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol. 15 No. 1 (2024): Maret
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v15i1.802

The fuel commonly used by people for daily needs is Liquefied Petroleum Gas (LPG). LPG leaks can occur in closed spaces with temperatures above 30℃ because they contain very dangerous propane and butane compounds. If there is no early warning, the gas cylinder can expode and cause a major fire. The proposed model is capable of displaying gas concentrations and air heat (temperature and humidity) and reporting via telegram using the fuzzy mamdani algorithm. The aim of the research is to design a model for monitoring gas leaks and air heat. The sensors used in this system are tehe MQ6 sensor for gas detection, DHT22 for temperature and humidity detection, fire sensor, and nodeMCU as an Internet of Things-based processor using the fuzzy logic method. This system will turn on fan, buzzr and send a warning to telegram automatically when conditions are unsafe and dangerous. The conclusion of the research is that buzzer warning and telegram notifications make it easier for users to identify gas leaks so they can take immediate action

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