Judul: Deteksi Anomali Cerdas untuk Tipikal Aliran Data Sensor IoT
Abstrak: Salah satu pemanfaatan internet of things adalah data logging pada sensor. Data logging pada sensor memudahkan orang untuk memonitor segala aktivitas lingkungan. Anomali didefinisikan sebagai suatu kejadian dimana pembacaan data pada sensor IoT yang tidak mengikuti pola bacaan aliran data sebelumnya. Terkadang sensor tidak memberi peringatan ketika terjadi anomali. Hal ini menyebabkan sulitnya deteksi anomali pada data yang dikirimkan sensor. Penelitian ini bertujuan mengembangkan model deteksi anomali cerdas untuk tipikal aliran data sensor IoT dengan menggunakan algoritme deep neural network sebagai model pendeteksi anomali. Jenis arsitektur deep neural network yang digunakan adalah autoencoder. Evaluasi model prediksi untuk kelompok sensor 1, 2, dan 3 diperoleh f1-score masing-masing sebesar 0.866, 0.654, dan 0.8. Parameter batas error yang digunakan agar diperoleh nilai tersebut masing-masing adalah 0.09, 0.055, dan 0.07.
Keyword: data logging, deep neural network, internet of things, f1-score, batas error

Judul: A Design of Portable Smart Surveillance Device Based on IoT for Perishable Goods Logistics
Abstrak: During transportation, restricted surveillance processes have triggered discrepancies, especially for perishable goods highly vulnerable to environmental factors. Conventional fleets are unable to monitor real-time delivery and goods conditions. Late in capturing and informing abnormal circumstances, causing product spoilage is arduous to prevent. The establishment of technology 4.0, such as the emergence of the internet of things (IoT) and artificial intelligence, offers solutions. This study presented a design of a smart surveillance device for perishable goods logistics based on IoT. Firstly, a requirements analysis is the basis for developing a prototype of the IoT system to monitor the transportation process in real-time. Then, one machine learning technique, specifically artificial neural network (ANN) predicted the remaining shelf life and the engine state during the shipment process. Finally, a developed warning mechanism establish to deal with abnormal conditions, such as sudden coolant failure or fraud in refrigerator settings. The prototype development deployed DHT sensors, SW-420 sensors, a GPS module, and a microcontroller to display all smart surveillance features proposed in this study, specifically, temperature, humidity, current fleet location, shelf life, engine state, and early warning in real-time. ANN model predicted the quality of goods, represented by the remaining shelf life and engine state, on or off, well with an accuracy of 97,96% and 92,93%, respectively. It is necessary to add other relevant environmental parameters to the predictive modeling of the quality of goods. This work was helpful for logistics managers in ensuring the quality of goods to the customers., Proses pemantauan yang terbatas selama transportasi memicu beberapa ketidaksesuaian, terutama untuk barang yang mudah rusak dimana sangat rentan terhadap faktor lingkungan. Armada konvensional tidak mampu memantau pengiriman dan kondisi barang secara real-time. Keterlambatan dalam menangkap dan menginformasikan kondisi abnormal menyebabkan kerusakan produk sulit untuk dicegah. Kehadiran teknologi 4.0, seperti internet of things (IoT) dan kecerdasan buatan, menawarkan solusi untuk permasalahan ini. Penelitian ini menghadirkan rancangan perangkat pemantauan cerdas untuk logistik barang yang mudah rusak berbasis IoT. Pertama-tama, analisis kebutuhan dilakukan untuk mengembangkan prototipe berbasis sistem IoT yang mampu memantau proses transportasi secara real-time. Salah satu teknik pembelajaran mesin, khususnya jaringan saraf tiruan (JST), digunakan untuk memprediksi sisa umur simpan dan status mesin selama proses pengiriman. Untuk mengatasi kondisi abnormal, seperti kerusakan pendingin mendadak atau penipuan dalam pengaturan pendingin, mekanisme peringatan dikembangkan. Prototipe yang dikembangkan dengan menggunakan sensor DHT, sensor SW-420, modul GPS, dan mikrokontroler telah mampu menampilkan semua fitur pemantauan cerdas yang diusulkan dalam penelitian ini yaitu temperatur, kelembaban, lokasi armada saat ini, umur simpan, status mesin, dan peringatan dini secara real-time. Model JST memprediksi kualitas barang, yang direpresentasikan oleh sisa umur simpan, dan kondisi mesin, hidup atau mati, dengan akurasi masing-masing 97,96% dan 92,93%. Untuk memperkaya efikasi penelitian ini, perlu ditambahkan parameter lingkungan lain yang relevan dengan pemodelan prediktif kualitas barang. Penelitian ini bermanfaat bagi manajer logistik untuk memastikan kualitas barang kepada pelanggan.
Keyword: ANN, IoT, perishable goods, smart surveillance

Judul: Biology and ecology of the buffalo fly, haematobia exigua de meijere (diptera:muscidae)
Abstrak: The life history of the buffalo fly was observed at room temperature of 27°-29.5°C and at a relative humidity of 48-68 percent. The eggs hatched after 17-22 hours, with an average of 18 hours. First instar larvae measured 1.25 + 0.16 mm long and 0.27 + 0.037 mm wide. reach the second larval stage. It took 6-12 hours to the second instar larvae measured 2.676 +0.187 mm long and 0.353 +0.028 mm wide. The second instar took about 12-24 hours to reach the third larval stage. The third instar measured 6.833 + 0.189 mm long and 0.919 + 0.051 mm wide. It took about 2-3 days for the third instar to become pupae. Duration of the pupal stage was about 5-7 days before the adults emerged. Under laboratory condition, the buffalo fly completed its life cycle in 9.5 to 10.5 days, whereas under field condition it took 9-14 days with an average of 10.5 days. The growth ratio which was based on the measurements of the cephalo-pharyngeal skeleton of each instar was 1.797. The preoviposition period was 8-12 days and mating usually occurred on the 6th or 7th day. The longevity of the male was 29 days, whereas it was 32 days in the female. The range of egg production under laboratory condition was between 20-139 per female fly. The belly and hind legs of the carabao were the most preferred habitat either during cloudy and cool dry or shiny and warm day. When the weather was clear and the carabao was in the shade, the flies tended to be more distributed particularly on the shoulder, dewlap, back and horns. The carabaos had relatively fewer Musca sp. and Stomoxys calcitrans than cattle; never the less carabaos had a higher infestation of buffalo fly than cattle. Oviposition occured either during the day or night but the buffalo flies were more active during the night than in the day time. The effect of various lengths of exposure time of manure in relation to the oviposition was profound. observed, the maximum number of the buffalo flies was occurred when the dung was exposed for 30 minutes. With increasing time of exposure the number and variety of insects also increase. It was evident that Sepsidae and psychodidae were the most dominant insects emerging from the dung. The number of buffalo flies also increased with increasing
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