Judul: Implementation of Internet of Things and Temperature Anomaly Detection Using Deep Learning Algorithms in Melon Fruit Distribution
Abstrak: Melon adalah tanaman holtikultura yang berpotensi meningkatkan perekonomian Indonesia dari sektor pertanian. Pertumbuhan ekonomi diipengaruhi oleh beberapa faktor salah satunya yaitu kegiatan ekspor. Ekspor hasil produksi pertanian khususnya melon dapat menjadi pilihan dalam meningkatkan perekonomian Indonesia. Namun, hal tersebut harus terlebih dahulu memperhatikan standar mutu yang ditetapkan oleh pemerintah dan negara tujuan ekspor. Oleh karena itu, upaya peningkatan perekonomian tersebut harus disertai dengan peningkatan kualitas produksi dan distribusi buah melon hingga sampai ke konsumen. Salah satu cara dalam menjaga kualitas buah melon adalah mengkombinasikan proses pemantuan suhu penyimpanan buah dan distribusi dengan penggunaan sensor suhu. Penggunaan sensor suhu dengan memanfaatkan teknologi internet of things dalam melakukan pemantauan suhu melon selama proses distribusi merupakan bentuk inovasi teknologi. Penelitian ini bertujuan untuk mengembangkan sistem distribusi buah melon dengan menerapkan perangkat IoT dalam melakukan pemantauan suhu gudang penyimpanan dan deteksi anomali suhu sebelum data tersebut ditransfer ke dalam sistem blockchain. Metode deteksi anomali suhu pada penelitian ini menggunakan algoritma deep learning. Autoencoder dipilih sebagai model arsitektur pada penelitian ini karena metode ini dapat membantu meminimalisir anomali data khususnya suhu. Hasil dari penelitian ini menunjukkan teknologi internet of things dan deteksi anomali suhu berhasil diterapkan dan berjalan dengan sangat baik. Berdasarkan hasil pengujian perangkat IoT diperoleh nilai kesalahan untuk suhu sebesar 8,26%. Hasil pengujian kinerja dengan parameter uji quality of service yaitu throughput sebesar 86,152 bps, delay sebesar 0,041199 ms dan packet loss 0,064%. Adapun hasil evaluasi model deteksi anomali suhu untuk precision, recall dan f1-score yaitu 0,9952, 1 dan 0,9658., Melon is a horticultural plant with the potential to boost Indonesia's economy in the agricultural sector. Economic growth is influenced by several factors, one of which is export activities. Exporting agricultural products, especially melons, can be an option to enhance Indonesia's economy. However, this must first adhere to the quality standards set by the government and the export destination country. Therefore, efforts to improve the economy must be accompanied by improvements in the quality of melon production and distribution to consumers. One way to maintain the quality of melons is by combining temperature monitoring during storage and distribution using temperature sensors. Using temperature sensors with Internet of Things (IoT) technology to monitor melon temperatures during distribution represents an innovation in technology. This research aims to develop a melon distribution system by implementing IoT devices to monitor warehouse temperatures and detect temperature anomalies before transferring data into the blockchain system. The temperature anomaly detection method in this study utilizes deep learning algorithms. Autoencoders were chosen as the architectural model in this research because they help minimize data anomalies, particularly temperature-related ones. The results of this study demonstrate successful implementation of IoT technology and temperature anomaly detection. Based on the testing results, the IoT device achieved an error rate for temperature of 8,26%. Performance testing results with quality of service parameters, throughput was measured at 86,152 bps, delay at 0.041199 ms, and packet loss at 0.064%. The evaluation results of the temperature anomaly detection model for precision, recall, and F1-score were 0.9952, 1, and 0.9658, respectively.
Keyword: Internet of Things (IoT), deep learning, deteksi anomali, autoencoder

Judul: Backend Development for Implementing the Netted Melon Maturity Detection Model
Abstrak: Melon merupakan salah satu buah yang diminati oleh masyarakat Indonesia, tetapi budidaya melon sering mengalami kendala dalam menentukan waktu panen yang tepat. Penelitian sebelumnya telah membuat model machine learning menggunakan ekstraksi ciri GLCM (Gray Level Co-Occured Matrix), fitur jala, dan classifier RF (Random Forest) dan SVM (Support Vector Machine) untuk mendeteksi kematangan melon berjala (Cucumis melo L). Namun, penggunaannya masih terbatas karena membutuhkan pemahaman teknis. Oleh karena itu, penelitian ini dibuat untuk memudahkan pengguna dengan mengimplementasikan model ke dalam aplikasi. Penelitian ini berfokus pada pengembangan back-end untuk mendukung aplikasi. Metode yang digunakan adalah tahapan SDLC (Software Development Life Cycle) model waterfall. Hasil penelitian menunjukkan bahwa program ekstraksi fitur hingga normalisasi berhasil diimplementasikan dan dua endpoint dibuat serta diuji dengan blackbox testing. Endpoint ini memudahkan pengguna mengunggah gambar melon dan menerima prediksi kematangan tanpa memerlukan pemahaman teknis mendalam. Pengujian kinerja menunjukkan total waktu rata-rata untuk mengunggah gambar hingga prediksi selesai adalah 445.2 ms, dengan waktu unggah 208.1 ms, dan waktu prediksi 200 ms., Melon is one of the fruits favored by the Indonesian community, but melon cultivation often encounters challenges in determining the right harvest time. Previous research has developed a machine learning model using GLCM (Gray Level Co-Occurred Matrix) feature extraction, net features and RF (Random Forest) and SVM (Support Vector Machine) classifiers to detect the ripeness of netted melons (Cucumis melo L). However, its usage is still limited as it requires technical understanding. Therefore, this research aims to facilitate penggunas by implementing the model into an application. This research focuses on Back-end development to support the application. The method used is the waterfall model of the SDLC (Software Development Life Cycle) stages. The results show that the feature extraction and normalization program was successfully implemented, and two endpoints were created and tested with blackbox testing. These endpoints enable penggunas to upload melon images and receive ripeness predictions without requiring deep technical knowledge. Performance testing shows that the total average time for uploading an image to completing the prediction is 445.2 ms, with an upload time of 208.1 ms, and a prediction time of 200 ms.
Keyword: Backend, melon, REST API, Waterfall, glcm

Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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