Publication Search

This study aims to develop tempeh (Rhizopus oryzae) into tempeh flour to be utilized as the main ingredient in the production of a traditional Indonesian cookie known as kastengel. Tempeh is a rich source of plant-based protein and dietary fiber, yet its application in the processed food industry, particularly in dry baked goods such as cookies, remains underexplored. This innovation is expected to serve as a healthy food alternative while supporting the diversification of local food products. The research employed a Research and Development (R&D) approach, encompassing several key stages: processing tempeh into flour through slicing, drying, grinding, and sieving; formulating kastengel dough using tempeh flour as the base ingredient; and evaluating the final product through both organoleptic (sensory) testing and nutritional analysis. Organoleptic testing involved 10 panelists, including lecturers and students, who assessed the product based on color, taste, aroma, and texture. Nutritional content was analyzed at the Manado Industrial Research and Standardization Agency. The results showed that kastengel made from tempeh flour had a light brown color, a savory-sweet flavor, a distinctive yet pleasant aroma, and a crunchy texture. Laboratory tests revealed the nutritional composition of the product to include 46.12% carbohydrates, 29.82% fat, and 3.92% protein, indicating its potential as a nutritious food option. Most panelists reported a favorable response toward the product. In conclusion, tempeh flour can serve as an effective substitute for wheat flour in the production of kastengel cookies. The final product, branded as “Kastengel Eka,” demonstrates that tempeh-based food innovations can be well-accepted in terms of sensory qualities and possess promising potential as a functional, nutritious, and marketable food product.

Photogrammetry is a technique for measuring and modeling three-dimensional objects by utilizing digital imagery from various perspectives. In the context of reverse engineering, this technique serves to duplicate, reconstruct, and analyze the dimensions of physical objects with a high degree of accuracy. The main advantage of photogrammetry lies in its ability to capture the details of the shape and texture of objects without the need for physical contact. However, the quality of photogrammetry scan results is greatly influenced by a number of technical factors, especially lighting and camera sensor sensitivity (ISO) settings. Variations in these two parameters can cause deviations or dimensional deviations in the resulting 3D model. This study aims to quantitatively evaluate the influence of lighting intensity and camera ISO setting on dimensional deviation in photogrammetry scan results. The research method used is experimental, where the dimensions of the scanned object are compared to the original dimensions using precision measuring instruments. The results showed that both the lighting level and the ISO setting had a significant influence on the accuracy level of the 3D model. The ideal lighting intensity range was found to be in the range of 125–150 lux, where shadows and light reflections could be minimized. Meanwhile, the use of low ISO (around 200) is able to produce cleaner image textures and minimize noise, resulting in smaller dimensional deviations. Additionally, the interaction between moderate lighting and low ISO is proven to provide the best scanning accuracy. This combination is able to maintain a balance between image quality and surface detail of the object. These findings not only provide practical recommendations regarding the regulation of scanning conditions, but can also serve as a guideline for industry practitioners and academics in improving the quality of reverse engineering results. With a proper understanding of lighting and ISO variables, the photogrammetry process can be optimized to produce more accurate and efficient 3D models.

The Message Queue Telementary Transport (MQTT) protocol is able to adjust the sending and receiving of messages to monitor in accordance with the user's preferences because the sending and receiving of messages is topic based on a specified topic, making it necessary to routinely monitor the condition of patients who have been diagnosed with heart problems from a distance. With the aim to perform a Quality of Service (QoS) analysis with throughput, delay, and packet loss parameters using Unshielded Twisted Pair internet transmission media (UTP) and Wireless, the goal of this research is to design and implement (MQTT) a heart rate monitoring device with an EKG module as a sensor and ESP32 as a microcontroller. On the Ubidots website, EKG signals are transmitted over the internet and shown in real time. QoS analysis is performed using the Wireshark application. Data was collected on two scenarios at intervals of 30 minutes, 1 hour, 2 hours, 5 hours, 8 hours, 12 hours, 18 hours, and 23 hours. The throughput, latency, and packet loss metrics used in this study's results cause different value variations; these are influenced by the weather, internet bandwidth, computer, and router specifications. According to testing, the tool is portable and has a 3000mAh battery, but it has the restriction that it can only be used with reliable internet and bandwidth.

Mental health is a crucial aspect of modern life, with stress and anxiety being among the most common and impactful psychological disorders. This research proposes a stress and anxiety monitoring system based on the Internet of Things (IoT), integrating biometric sensors and Deep Neural Networks (DNN) for early detection and in-depth analysis. The system is designed using MAX30102 (heart rate and SpO₂), GSR (Galvanic Skin Response), and DS18B20 (body temperature) sensors, managed by an ESP32 microcontroller and communicating through the MQTT protocol. Physiological data is collected in real-time, formatted in JSON, and transmitted to both Android and web-based applications for visualization. The DNN model is developed using the TensorFlow framework with a layered architecture and ReLU activation functions to classify four mental states: relaxed, calm, anxious, and highly stressed. The training dataset comprises both primary and secondary data, including the WESAD dataset. Model performance is evaluated through k-fold cross-validation, showing high accuracy and strong generalization capabilities. The results indicate that the integration of sensor technology and deep learning significantly improves the effectiveness of stress and anxiety detection compared to traditional methods. This system demonstrates great potential for the development of AI-based wearable devices for autonomous, real-time, and adaptive mental health monitoring. 

Abstrak  - Coal handling system berfungsi menangani pekerjaan mulai dari pembongkaran batu bara dari kapal/tongkang (unloading area) yang dipindahkan ke conveyor - conveyor, penimbunan/penyimpanan di stock area, ataupun pengisian ke bunker yang digunakan untuk pembakaran di boiler. Dalam proses pembongkaran tersebut tidak boleh terjadi penyumbatan / plugging terutama pada chute conveyor, maka dari itu diperlukan sensor proteksi untuk mencegah hal tersebut. Sensor lama yang digunakan yaitu berjenis Tilt Switch yang selama pemakaian tidak efektif karena banyak masalah sehingga dilakukan pemeliharaan, akibatnya dilakukan penggantian menggunakan sensor berjenis MBD (Microwave Beam Detector). Dari analisa setelah penggantian menggunakan sensor MBD, permasalahan penyumbatan chute berkurang dapat dilihat dari frekuensi pemeliharaan yang semakin berkurang.

The food security program involving chili cultivation in Pentur Village is hindered by inefficiencies in water use and suboptimal plant growth, primarily due to traditional irrigation methods that fail to consistently maintain ideal soil moisture and temperature. This issue is exacerbated by unpredictable environmental shifts, such as fluctuating weather patterns, and a lack of precise irrigation control stemming from technological limitations. To address this, a system for monitoring and regulating chili plant irrigation using IoT technology was developed. This system employs humidity and temperature sensors connected to an IoT platform like Blynk, enabling real-time observation of plant and environmental conditions. Data on soil moisture, air temperature, and humidity are stored in a database, and irrigation is automated based on soil moisture levels. The goal is to enhance water efficiency, minimize risks associated with over or under-watering due to environmental variations, and improve both yield and quality of the chili crop. This IoT-based system aims to simplify chili plant management for Pentur Village farmers and significantly boost agricultural output.

This research aims to be able to meet the water supply of lettuce plants automatically by using three sensors such as soil moisture, water level, and water discharge. The goal is to provide water needs to plants automatically and regularly. The developed tool uses YL-96 sensor for soil moisture, HC-SR04 for water level and YF-S201 for water discharge. Sensor data is sent to the arduino to be processed using the fuzzy mamdani method so that these three data values affect the movement of the tap servo motor that flows to the lettuce plant. Fuzzy logic here as a decision maker from the value of 3 sensor data and then processed automatically by arduino using fuzzy mamdani to determine how many degrees the servo motor moves. The result is that the Lettuce Plant Water Needs Analysis System Automation Tool is able to maintain the water supply of lettuce plants and soil moisture ideally at 76% with a servo motor movement system success rate of 100%.

Due to the busy activities outside the home, some people do not have time or forget to water their plants, so when they return home they find that the plants have died from drought. Therefore, to overcome this problem by conducting research using Internet of Things technology. The aim of this research is to design an automatic watering tool to overcome manual watering and support learning. This prototype uses a NodeMCU ESP8266 as the main controller, a soil moisture sensor is used to read soil moisture.  Soil moisture sensors are used to detect soil moisture. If the soil humidity is below the minimum limit, the watering process will be active and the watering process will be active if the NodeMCU ESP8266 receives commands from the smartphone.

Indoor positioning technology based on smartphones plays an important role in the current technological development context. Especially in applications such as warehouses, supermarkets, hospitals, or buildings. While the global positioning system (GNSS) is popular and effective outdoors, it has several limitations when operating in enclosed spaces, such as indoors, due to the complexity of these environments. Smartphones have many built-in sensors (such as light sensors, sound sensors, gyroscopes, accelerometers, and magnetic sensors) and support the connection of various types of wireless communication technologies such as Wi-Fi and Bluetooth. However, such sensors were not initially developed for positioning applications. This study addresses the positioning problem using the MUSIC technique in conjunction with the Time of Arrival (ToA) method. The effectiveness of the positioning solution is evaluated through the signal-to-noise ratio (SNR) index. The absolute error and squared error indices are evaluated through the cumulative distribution function (CDF) to indicate the effectiveness of the proposed solution. Additionally, we propose a Pedestrian Dead Reckoning method to determine a person's position in indoor environments continuously. Based on the segmentation of the moving process by turns, the direction measurements in each segment are processed using a Kalman filter, which is designed to enhance the results achieved by the system. We also discuss the challenges and some future research directions in the field of smartphone-based indoor positioning.

Deteksi nyeri objektif merupakan tantangan dalam dunia medis, terutama bagi pasien yang tidak mampu mengungkapkan rasa sakit secara verbal. Dengan kemajuan teknologi sensor dan kecerdasan buatan, sistem otomatis untuk mendeteksi nyeri berbasis sinyal fisiologis dan ekspresi wajah mulai dikembangkan. Studi ini bertujuan mengidentifikasi tren, metode, dan kualitas metodologis dari penelitian yang menggunakan database publik seperti BioVid Heat Pain, UNBC-McMaster, dan SenseEmotion dalam pengembangan sistem deteksi nyeri berbasis sensor. Penelitian dilakukan dengan pendekatan Systematic Literature Review (SLR) berdasarkan protokol PRISMA 2020 melalui pencarian artikel di Google Scholar dalam rentang tahun 2015–2024. Setelah seleksi berdasarkan kriteria inklusi dan eksklusi, 26 studi dimasukkan ke dalam sintesis naratif. Data dianalisis berdasarkan jenis sensor, metode algoritma, akurasi, dan ukuran sampel, serta dievaluasi menggunakan pendekatan GRADE. Hasil menunjukkan bahwa BioVid dan UNBC-McMaster adalah database paling sering digunakan, dengan sensor EDA, EMG, dan ekspresi wajah sebagai modalitas dominan. Metode klasifikasi umum mencakup CNN, SVM, dan Random Forest. Studi menyimpulkan bahwa pendekatan multimodal dan deep learning meningkatkan akurasi deteksi nyeri, namun validasi klinis dan perhatian terhadap keragaman demografis masih dibutuhkan.

 The Karanggeneng River in Rembang Regency, Central Java, serves as the main water source for the surrounding community but is vulnerable to seawater contamination during the dry season due to decreasing river elevation. To address this issue, this study aims to design and implement a river water elevation monitoring device based on the Internet of Things (IoT) powered by a hybrid Solar Power Plant (PLTS). The device utilizes the MB7360 ultrasonic sensor connected to an ESP32 microcontroller to measure water elevation in real-time and display the data through an LCD and the Blynk application on a smartphone. The methodology includes literature review, device design, system implementation, and field performance testing. Test results show that the sensor can measure water height accurately within a range of 30 cm to 5 meters, and the PLTS system is capable of supplying the required 0.56 Watts of power. The study compared two alternative solutions and selected the ESP32-based system as the best option due to its efficiency, cost-effectiveness, and easy-to-source components. The conclusion of this research indicates that the developed device can provide accurate and continuous information, support monitoring of river conditions to prevent the risk of seawater intrusion, flooding, or drought, and has the potential to be applied as a mobile system in various other river locations across Indonesia.

This study aims to explore the behavioral dynamics of Generation Z (Gen Z) toward digital culinary brands on TikTok, a platform increasingly central to youth consumer engagement. The research investigates how Gen Z interacts with, interprets, and forms relationships with culinary content in a digital environment shaped by algorithmic trends and participatory culture. Employing a qualitative phenomenological approach, data were collected through in-depth interviews with 12 Gen Z participants aged 18–24 years who actively engage with culinary brand content on TikTok. Additional data were obtained from observational analysis of brand-related content and user interactions on the platform. Thematic analysis revealed three core themes: emotional engagement with sensory-rich food content, digital consumption as identity performance, and expectations of brand authenticity and interactivity. Participants expressed strong affective responses to visually appealing and immersive content, linked their brand preferences to personal values and social identity, and favored brands that demonstrated responsiveness, humor, and human-like interaction. These findings highlight the role of TikTok not only as a marketing tool but as a cultural space where brand perception and loyalty are socially constructed. This study contributes to the theoretical understanding of consumer engagement by situating Gen Z’s behavior within a sociocultural and digital framework. Practically, it offers insights for culinary brands to develop more authentic, interactive, and value-driven content strategies. The findings also suggest implications for digital marketing policies and content regulation. Future research is recommended to explore cross-platform behaviors and cultural variations in digital brand engagement.

A 300 kVA generator set is an important tool as a backup power source in many sectors. Manual monitoring of generator set performance is often inefficient and can cause delays in detecting problems. Various advances in Internet of Things (IoT) technology have enabled the design of remote monitoring systems to overcome these limitations. This study aims to develop an IoT-based monitoring tool capable of monitoring important generator set parameters such as voltage, current, power, and engine temperature in real-time. This will enhance generator set management efficiency and reduce the risk of damage. The method used is an experimental approach involving the creation of an IoT-based monitoring system prototype. Operational data from the generator set is collected using sensors and then transmitted to an IoT platform for analysis. This research uses PZEM-004T sensors to monitor parameters such as power, current, and voltage. RTD PT100 sensors monitor parameters such as engine temperature, oil temperature, and turbo temperature. Honeywell PX3 sensors monitor oil pressure. Honeywell 1GT101DC sensors monitor engine speed. The results of the study show that the device works well with an average error of 0.76% from the sensors used. The greater the distance between the Wi-Fi modem and the device, the slower the data response speed.

Pasta dough, or fresh pasta, is a flour-based food product that is popular globally but has limitations in shelf life. This study aims to develop an innovative pasta dough by adding kluwek (Pangium edule), a local Indonesian ingredient, to enhance flavor, texture, aroma, and color. Kluwek was chosen due to its bioactive compounds, such as tannins, healthy fats, and minerals, as well as its potential as a natural flavor enhancer (umami) and preservative. This experimental study used variations of kluwek addition (8%, 10%, 12%) to the basic pasta dough (wheat flour, egg, salt). Data were collected through organoleptic tests by 30 panelists (trained and semi-trained) to evaluate color, aroma, taste, texture, and overall preference, as well as laboratory tests for proximate analysis (moisture, protein, fat, fiber, ash, carbohydrates, and tannins). The organoleptic test results showed that the 12% kluwek concentration produced pasta with a moderately brown color (score: 3.10), a moderate aroma (3.07), a sufficiently savory taste (3.03), and a chewy texture (3.43), which was significantly preferred over the 8% and 10% concentrations (p<0.05). Proximate analysis revealed superior nutritional content, including protein (13.80%), healthy fats (5.10%), fiber (2.05%), and tannins (0.41%). The moisture (24.05%) and ash (0.31%) levels met the SNI 3551:2015 standard for fresh pasta. In conclusion, the addition of 12% kluwek produces pasta with acceptable sensory properties and higher nutritional value. This innovation not only enriches the diversification of locally sourced pasta products but also opens opportunities for the commercialization of functional foods. Recommendations for further research include shelf-life stability testing, cyanide residue toxicity assessment, and formulation optimization with other additives.  

Methane gas detection is crucial in the oil and gas sector to enhance safety and operational efficiency. This study examines the impact of three types of gas detectors—catalytic, infrared, and ultrasonic sensors—on accuracy and response time. The research was conducted at PT PHM's onshore and offshore sites to evaluate sensor performance in operational environments. A quantitative approach with direct field observation was used. Data were collected by measuring methane gas concentrations indicated by detectors, which were then compared to standard gas concentrations. Response times were recorded when the detectors were exposed to methane concentrations of 2.5% LEL until the alarm triggered at 40% of full scale. Data analysis was performed using descriptive statistics, homogeneity test, normality test, ANOVA, and post hoc tests. The results show that the infrared detector had a response time of 2.87 seconds with an accuracy of 0.218%. The catalytic detector had a response time of 8.91 seconds and accuracy of 0.489% and the ultrasonic detector had a response time of 6.15 seconds and accuracy of 0.842%. Overall, the infrared detector demonstrated the best performance in response time and is recommended for use at PT PHM.

In the modern era, the need for sophisticated vehicle security systems is increasing along with the high rate of motor vehicle theft. This research designs and implements a fingerprint sensor-based vehicle security system using an Arduino Uno microcontroller. This system aims to improve security by utilizing biometric technology that can only be accessed by verified users. The fingerprint sensor is used to recognize the user's fingerprint, then activate the system through a relay instead of a conventional key. System testing shows a fast response time with an estimated rise time of about 0.5 seconds and settling time of about 2 seconds, without any misidentification (false positive or false negative). Thus, the system is proven to provide higher security, good authentication speed, and ease of use compared to conventional security systems. These results show that the implementation of biometric technology in vehicles has the potential to be widely applied.

Proses budidaya jamur tiram sangat tergantung dengan kestabilan pada kondisi lingkungan, terutama suhu ruangan dan kelembapan yang harus di perhatikan oleh para petani.  Hal ini menjadi permasalahan ketika proses pemantauan dan pengendalian lingkungan secara manual dilakukan, membutuhkan tenaga yang kuat dan waktu yang cukup besar. Penelitian ini bertujuan Mendesain rancangan sistem monitoring dan kendali suhu ruangan budidaya jamur tiram secara otomatis dan jarak jauh berbasis teknologi Internet of Things (IoT) untuk para petani. Dalam proses sistem ini penelitian ini memanfaatkan sensor suhu dan kelembapan DHT11 sebagai input, mikrokontroler Arduino Uno sebagai pemroses data, dan modul ESP8266 sebagai pengirim data nirkabel ke aplikasi Android berbasis Blynk.  Adapun metode dalam penelitia ini digunakan pengembangan yang digunakan adalah Software Development Life Cycle (SDLC) model waterfall, dan menganalisis kebutuhan, perancangan sistem, implementasi, pengujian, hingga pemeliharaan. Hasil pengujian menunjukkan bahwa sistem mampu membaca suhu dan mengaktifkan blower (kipas) secara otomatis ketika suhu melebihi ambang batas, serta menampilkan data suhu dan status kipas secara real-time melalui aplikasi Blynk. Dengan adanya sistem ini, pemantauan dan pengendalian lingkungan budidaya jamur dapat dilakukan lebih efisien dan fleksibel dan mendukung produktivitas budidaya secara optimal.

Engine overheating is a critical condition that can cause damage to internal components, reduce operational efficiency, and lead to overall system failure. The absence of an automatic protection system is one of the primary factors contributing to damage. This study aims to design and test a temperature sensor-based safety device that can automatically cut off the engine's working system when the temperature exceeds the safe limit. The method used is descriptive statistical analysis to evaluate the effect of independent variables (operational time duration and workload) on the dependent variable (coolant temperature). The system was tested through water heating simulation using an electric heater and controlled by an Arduino Nano microcontroller, a MAX6675 temperature sensor. Testing was carried out with variations in power load (150, 300, 450, 600, and 750 Watts) and operational duration (20, 40, and 60 minutes). Temperature data were collected and analyzed using ANOVA to determine the effect of load and time on temperature increase. The results showed that the temperature increased significantly with increasing power load, with temperatures approaching 100°C at loads ≥450 Watts in less than 20 minutes. The load variable shows a significant effect on temperature (p < 0.05), while the duration of time shows a nonlinear but not statistically significant upward trend. The safety device is proven to be able to automatically disconnect the system when the temperature reaches the specified maximum limit, thus effectively preventing engine damage due to overheating.

Coffee ground have great potential to be used as an additional ingredient in making biscuits. The purpose of this study is to create innovative coffee grounds biscuits that can be a promising business opportunity. This study uses a Research and Development (R&D) research design with a 4D model (Define, Design, Develop, and Disseminate). The results of this study are BIS'KOP (Biskuit Ampas Kopi) products that have a sweet taste, light coffee aroma, crunchy texture, and brown color on the biscuit part and white color on the vanilla cream part. Overall, the sensory characteristics of BIS'KOP products, including taste, aroma, texture, and color have met the expected criteria and are worthy of being marketed. BIS'KOP products can be sold at a price of IDR 24,000.00 per package containing 5 (five) pcs of BIS'KOP. Each serving of BIS'KOP can meet 5% of energy needs, 2% of protein needs, 7% of fat needs, and 5% of carbohydrate needs. Thus, it can be concluded that coffee grounds biscuits have the potential as an alternative snack with a zero waste concept.

The objective of this study was to assess the sensory acceptability of Italian Meringue Butter Cream in terms of texture, aroma, color, and taste, using varying proportions of sweeteners. The research was carried out in the Culinary Arts Laboratory of the Ibu Kartini Academy of Social Welfare. An experimental approach was employed, utilizing a Completely Randomized Design (CRD). The study involved three treatments with different ratios of granulated sugar to stevia: F1 (220 g : 20 g), F2 (150 g : 25 g), and F3 (80 g : 30 g). A total of 35 untrained panelists participated in the hedonic sensory evaluation. The resulting data were analyzed using the non-parametric Kruskal-Wallis test, followed by the Mann-Whitney U test at a 95% confidence level (α = 0.05). The findings revealed that the variation in sweetener proportions significantly influenced the panelists’ preferences regarding aroma, texture, color, and taste. Among all formulations, the F1 treatment received the highest level of overall acceptance.