Directory of Scientific Journals Indonesian Society of Applied Science (ISAS)
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Analisis Sifat Tanah Lempung Lunak yang Di Stabilisasi Dengan Limbah Ban Karet dan Fly Ash
Full text linkSoft soil in construction is often a problem. This is because the bearing capacity of the soil is very low. Various soil improvement methods have been developed, one of which is the soil stabilization method. This study aims to investigate the effect of the addition of waste rubber tires and fly ash as stabilizer materials on changes in the physical and mechanical properties of soft clay soil. In addition, the effect of the length of curing of the soil mixture with stabilizer was also observed. After identifying the clay soil locally, the clay soil was mixed with stabilizer materials at several percentages (5%, 10%, 15% and 20%), which were then cured for 7, 14, and 28 days before testing the physical and mechanical properties. The results revealed that rubber tire powder plus fly ash mixed in soft clay soil had a positive effect on the physical and mechanical properties of the soil. The results of the consistency limits test showed a decrease in moisture content by 53.23% and the plasticity index (PI) value of the soil by 27.32% from its original condition. This decrease depends on the length of the soaking period. In addition, the CBR test results also increased significantly. The largest data value was obtained at 20% stabilizer mixing with 28 days of curing time by 10.32%. This shows that rubber tire powder and fly ash can work well as a binder (pozzolan) because they can bind the soil so that the carrying capacity of the soil increases.Tanah lunak dalam konstruksi sering menjadi permasalahan. Hal ini disebabkan karena daya dukung tanah tersebut sangat rendah. Berbagai metode perbaikan tanah telah banyak dikembangkan, salah satunya dengan metode stabilisasi tanah. Penelitian ini bertujuan untuk menyelidiki bagaimana pengaruh penambahan limbah ban karet dan fly ash sebagai bahan stabilizer terhadap perubahan sifat fisik dan mekanis tanah lempung lunak. Di samping itu pengaruh lamanya pemeraman campuran tanah dengan bahan stabilizer juga diamati. Setelah mengidentifikasi tanah lempung secara lokal, tanah lempung tersebut dicampurkan dengan bahan stabilizer dengan beberapa persentase (5%,10%,15% dan 20%) yang selanjutnya di peram selama 7, 14 dan 28 hari sebelum dilakukan pengujian sifat fisis dan mekanisnya. Dari hasil penelitian mengungkapkan bahwa serbuk ban karet yang ditambah fly ash yang dicampurkan pada tanah lempung lunak berpengaruh positif terhadap sifat fisis dan mekanis tanah. Hasil pengujian batas-batas konsistensi menunjukkan terjadinya penurunan kadar air sebesar 53,23% dan nilai indeks plastisitas (PI) tanah sebesar 27,32% dari kondisi insialnya, Penurunan ini tergantung pada lamanya masa peram. Selain itu dari hasil pengujian CBR, juga mengalami peningkatan nilai secara signifikan. Nilai data terbesar diperoleh pada pencampuran bahan stabilzer 20% dengan waktu pemeraman 28 hari sebesar 10,32%. Hal ini menunjukkan bahwa serbuk ban karet dan fly ash dapat bekerja baik sebagai bahan pengikat (pozzolan) karena dapat mengikat tanah sehingga nilai daya dukung tanah meningka
Konsistensi Model Regresi Empat Variabel Pada Populasi dan Sampel untuk Prediksi Temperatur
Full text linkThe ability to predict future events or trends has become very important today. One method that can be used to predict the future is to use linear regression. Accurate regression modeling requires sampling representative data, especially when working with large datasets. This research takes a relatively large volume as a data set by looking at the accuracy and consistency of the coefficients of a multi-variable linear regression model for temperature prediction which is built based on all the data, and looks at the differences in the regression model built from the sample data. The number of sample data (n) is determined based on the Slovin formula which depends on the number of population data (N) and the level of confidence (ơ), so that as many as (N/n) new regression models can be built. Each group of sample data is divided into 75% for modeling and 25% testing data. The dataset used is weather information in the Szeged area which was measured in 2006 - 2016. So the regression model is in the form of Y (temperature value) which is influenced by Xi (weather factors), namely humidity, wind speed, wind direction and visibility. Using 96,453 data records and a 1% significance level in Slovin\u27s formula, 10 samples were generated. Nine out of ten sample regression models agree with the population model, with positive coefficients for visibility and wind direction and negative values for humidity and wind speed. There is an abnormality in sample #4. While the other nine sample regression models are consistent with positive R2 values, Sample #1 displays an oddity with negative values. The RMSE interval values for each regression model in this study fall between 4.334 and 9.582
Pengembangan Aplikasi Augmented Reality untuk Edukasi Keselamatan Kebakaran: Metode Prototyping dan Usability
Full text linkThis study aims to develop an Augmented Reality (AR) application to introduce fire safety equipment as part of Occupational Health and Safety (OHS) education for the general public. The research employed a prototyping method, which involved iterative stages from requirements analysis to user evaluation, as well as functional testing using Black Box Testing and usability testing with 20 respondents. The results showed that all application features functioned according to specifications, and the usability testing yielded a user satisfaction score of 80.1%. These findings indicate that AR is effective as an interactive educational medium to enhance public understanding of fire protection equipment. The implication of this study is the potential for wider use of AR technology in public education to support fire risk mitigation efforts
Penentuan Bahaya Longsor Berdasarkan Pedoman Penilaian Tingkat Risiko Lereng Jalan (Ruas Waipia-Saleman)
Full text linkLandslides are one of the most frequent geological disasters in Indonesia, leading to significant losses to infrastructure and the economy, especially in areas with particularly steep topography, such as Seram Island, Central Maluku. The island is susceptible to landslides due to a combination of natural factors such as high, heavy rainfall, mountainous topography, and seismic activity, as well as anthropogenic factors such as deforestation and agricultural activities on slopes. The objective of this study is to evaluate the landslide hazard in Seram Island (Waipia-Saleman Section) by identifying high-risk areas using the 2018 road slope assessment guidelines. From the analysis result along the Waipia-Saleman section, it was found that there were 6 points with very high potential for landslide, 1 point with a high potential for landslide, 4 points with medium potential for landslide, and 2 points with low potential for landslide. Mitigation recommendations at 6 spots with very high potential slopes were made to reconstruct the slopes. Instrument installation and rehabilitation were conducted at 1 spot with a high potential slope. At the 4 spots with medium potential slopes, rehabilitation was recommended as the mitigation, while the 2 spots with low potential slopes were assigned periodic maintenance.Tanah longsor merupakan salah satu bencana geologi yang sering terjadi di Indonesia, menyebabkan kerugian yang signifikan terhadap infrastruktur dan perekonomian, terutama di wilayah yang memiliki topografi curam seperti Pulau Seram, Maluku Tengah. Pulau ini rentan terhadap longsor akibat kombinasi faktor alam seperti curah hujan tinggi, topografi pegunungan, dan aktivitas seismik, serta faktor antropogenik seperti penggundulan hutan dan aktivitas pertanian di lereng. Tujuan dari penelitian ini adalah mengevaluasi bahaya tanah longsor di Pulau Seram dengan mengidentifikasi area berisiko tinggi dengan menggunakan pedoman penilaian lereng jalan 2018. Dari hasil analisis di sepanjang ruas Waipia-saleman didapatkan 6 titik lereng yang berpotensi sangat tinggi, 1 titik yang berpotensi tinggi, 4 titik lereng yang berpotensi sedang dan 2 titik lereng yang berpotensi rendah. Rekomendasi mitigasi pada 6 titik lereng yang berpotensi sangat tinggi adalah merekonstruksi lereng. Untuk pemasangan instrumen dan rehabilitasi dilakukan pada 1 titik lereng yang berpotensi tinggi. Sedangkan pada 4 titik lereng yang berpotensi sedang mitigasinya adalah rehabilitasi dan 2 titik lereng yang berpotensi rendah membutuhkan pemeliharaan rutin dan berkala
Evaluasi Kapasitas Daya Tampung Kolam Retensi Kecamatan Kemuning Kota Palembang Provinsi Sumatera Selatan
Full text linkOne of the largest cities, because it is the center of social and economic activities, is Palembang City. The area of Palembang City is around 400.61 km², consisting of 16 sub-districts and 107 villages. In this area, heavy rain has occurred which has caused flooding in this area, especially on Jalan Pipa Raja, Palembang City. One of the causes of flooding in Palembang is due to low land conditions and high tides of the Musi River. In the role of flood control, the lack of public awareness in maintaining the drainage system also worsens waterlogging. Flood control requires extensive and specific engineering knowledge. The structural method used in this study is the retention pond method. The retention pond on Jalan Pipa Jaya in Kemuning District has a capacity of around 328.8 m3/hour. Data for this study were obtained from literature studies, primary data, and secondary data. The results of the study that have been carried out indicate that in modeling using HEC-HMS, it is known that the Pipa Jaya retention pond can accommodate a peak inlet discharge of 161.3 m3/s, with a decrease to 29.7 m3/s.Salah satu kota terbesar karena menjadi pusat aktivitas sosial dan ekonomi yaitu Kota Palembang. Luas wilayah Kota Palembang sekitar 400,61 km² yang terdiri dari 16 kecamatan dan 107 kelurahan. Pada daerah tersebut telah terjadi hujan deras yang menyebabkan banjir di wilayah ini, terutama di Jalan Pipa Raja Kota Palembang. Salah satu penyebab banjir di Palembang disebabkan oleh kondisi lahan rendah dan tingginya pasang air sungai Musi. Dalam peran penganggulangan banjir kurangnya kesadaran penduduk dalam menjaga sistem drainase juga memperparah genangan air. Pengendalian banjir memerlukan pengetahuan teknik yang luas dan spesifik. Metode struktural yang digunakan dalam penelitian ini digunakan metode kolam retensi. Kolam retensi dijalan Pipa Jaya di Kecamatan Kemuning, memiliki kapasitas daya tampung sekitar 328,8 m3/jam. Data untuk penelitian ini diperoleh dari studi literatur, data primer, dan data sekunder. Hasil studi yang telah dilakukan diperoleh bahwa dalam pemodelan menggunakan HEC-HMS, diketahui bahwa kolam retensi Pipa Jaya dapat menampung debit masuk puncak sebesar 161,3 m3/s, dengan penurunan menjadi 29,7 m3/s
Rancang Bangun Sistem Pelacak Posisi Human Indoor Location menggunakan ESP-Now
Full text linkThis research aims to develop an Internet of Things (IoT)-based employee attendance and position tracking system using the ESP-NOW protocol integrated with a web database. This system is designed to automatically record attendance and monitor the position of employees in real-time using the Trilateration method with the Exponential Path Loss model. Data from the ESP32 device is sent to the MySQL database via the MQTT protocol and displayed in an easily accessible website interface. The results show that the system can efficiently recap daily, weekly, and monthly attendance data, as well as display the position history of employees and guests with sufficient accuracy. With the implementation of this system, the efficiency and accuracy of attendance management and employee monitoring at Sutami Hydroelectric Power Plant has increased significantly.
 
Implementasi UAV dan ArcGIS untuk Pemetaan 3D Kawasan Hutan Konservasi Ubadari
Full text linkThis study aims to produce a three-dimensional (3D) visualisation model of the Ubadari Conservation Forest Area in Fakfak Regency, West Papua, using Unmanned Aerial Vehicle (UAV) technology and ArcGIS Pro software. Aerial imagery data was collected through photogrammetric missions with calibrated parameters. This elevation model has high accuracy with a Root Mean Square Error (RMSE) value of 0.35 metres, indicating an average vertical deviation of only about 35 cm from the actual elevation value—accurate enough for conservation and advanced mapping applications. Spatial analysis was conducted to map topography, vegetation index (NDVI), land cover classification using the k-means clustering algorithm, as well as zones prone to degradation and potential fires. The results show that more than 15% of the area has a slope of >30%, and around 22.3% of the area is classified as having poor vegetation health. Meanwhile, 23.7% of the area was classified as low vegetation cover. Degradation and fire-prone zones covered 18.5% of the study area, mainly around road access and the edges of the area. These findings contribute to data-based monitoring systems and form an important basis for risk mitigation planning and conservation forest ecosystem preservation
Performance Comparison of Support Vector Machine (SVM) and k-Nearest Neighbors (kNN) in Verifying Material Orientation
Full text linkIn automated manufacturing, verifying material orientation is essential to ensure the product assembly proceeds without errors. For instance, in the beverage industry, incorrect orientation of materials, such as bottle caps, can lead to failures in the packaging process, resulting in improperly sealed bottles that may compromise product quality and safety. This study compares the performance of Support Vector Machine (SVM) and k-Nearest Neighbors algorithms for verifying material orientation verification through automated optical inspection. The images were processed using the Inception V3 Convolutional Neural Network (CNN) to extract relevant image features, which were then classified using SVM and kNN algorithms. As a result, SVM achieved high classification performance during testing, with classification accuracy, precision, recall, and F1 score of 1.0 compared to kNN, which achieved only 0.967. However, kNN demonstrated superior computational efficiency, with a training time of 1.126 seconds and a validation time of 0.713 seconds, compared to SVM\u27s training time of 3.101 seconds and validation time of 1.479 seconds. These results indicate that while both methods are highly effective for material orientation verification, kNN offers significant advantages in terms of computational speed, making it more suitable for real-time applications. The implications of this study highlight the potential for integrating the proposed method in industrial applications, promoting enhanced efficiency and reducing error rates in automated assembly lines
Penerapan Metode Profile Matching untuk Menunjang Keputusan Seleksi Penerimaan Anggota pada Perusahaan Marikator
Full text linkThe process of placing members in the XYZ organization is not optimal due to errors in placement or mismatches between fields and the positions assigned to its members. This is evidenced by members lacking maximum competence in their designated divisions, leading to slow organizational performance. Therefore, optimal human resource management in an organization requires selecting members with the right profiles to enhance operational performance. This study aims to determine a ranking and identify the best candidates for new member selection by applying the profile-matching method in the recruitment process of the XYZ organization. This approach considers criteria such as CVs, short essays, and interviews, using prospective member data comprising 19 (nineteen) individuals as the population and 5 (five) as the sample. Profile matching is a systematic approach for comparing the suitability of prospective member profiles against predefined criteria, enabling a more detailed and objective evaluation. This method helps identify competence gaps between candidates and expected criteria and minimizes the risk of placement errors within the organization. The calculation process is carried out using Microsoft Excel and a simple program developed with Python, utilizing Google Colab as the code editor. The study results indicate that the profile-matching method effectively identifies prospective members who best align with the organization\u27s qualifications. Among the five candidates evaluated, Candidate 1 achieved the highest final score of 4.0556, indicating the most optimal suitability with the established criteria
Rancang Bangun dan Evaluasi Sistem Sortir Otomatis Barang dengan Metode Deteksi Objek YOLO v5 dan Kendali PLC Outseal
Full text linkManual sorting in manufacturing is time-consuming, labor-intensive, and prone to errors, especially when items have similar colors and shapes. This study aims to design and implement an automatic sorting system for goods based on color and shape to enhance production efficiency. The system integrates a webcam for image acquisition, the YOLO v5 object detection algorithm for real-time classification, and the Outseal PLC to control actuators via a Ladder Diagram. An experimental method was used, with a dataset of 18 object types tested under three lighting conditions (daylight, low light, and ring light). Performance was evaluated using a confusion matrix, achieving an average accuracy of 88.26% and precision of 70.38%, with the best results under ring light illumination. These findings demonstrate that the proposed system can reduce operational costs and improve productivity for small- to medium-scale industries. Future work should include extended field testing and adaptive algorithms for varying lighting environments