Implementasi Mesin Pengeruk Isi Buah Markisa berbasis Mikrokontroler dan Elektro Pneumatik

Passion fruit has a distinctive sweet and sour taste, which is quite popular in Indonesia, especially in South Sulawesi. Passion fruit is processed into syrup, and dodol is one of the typical souvenirs popular with tourists. Processing passion fruit syrup in the Micro, Small, and Medium Enterprises (MSMEs) Industry still uses very simple equipment. Hence, the quality and quantity of the product produced are not optimal. Passion fruit cutting uses a knife with a low capacity for cutting and shrinkage results, so this research aims to design a passion fruit-filled shaver machine based on a microcontroller and electro-pneumatics. This passion fruit shaver machine uses a microcontroller as the control center. First, the passion fruit passes through an infrared sensor, which detects the fruit and counts the number of incoming fruits. After that, Arduino Uno reads and activates the control relay from the DC motor. After 0.5 seconds, cylinder 1 is active, which pushes the DC motor down so that the passion fruit shrinks for 3 seconds. Then, the pneumatic push up to the normal position; after 0.5 seconds, the second cylinder actively pushes the passion fruit skin that has been shaved out (thrown away). This processes for 1 second and returns to its normal position. If there is passion fruit, it comes in again, and the tool functions similarly, and so on. The results of this study produced a passion fruit shrinker machine with dimensions of length 300 mm x width 300 mm x height 600 mm, a motor power of 240 Watt or 0.321 HP, and a production capacity of 54 kg/hour.Buah Markisa mempunyai rasa khas asam manis yang populer di Indonesia, terutama Sulawesi Selatan. Buah Markisa diolah menjadi sirup dan dodol merupakan oleh-oleh yang disukai wisatawan. Proses pengolahan sirup markisa pada Industri UMKM masih menggunakan peralatan sederhana, sehingga kualitas dan kuantitas produk yang dihasilkan tidak maksimal. Pemotongan buah markisa menggunakan pisau dengan kapasitas hasil pemotongan dan pengerukan masih rendah sehingga tujuan penelitian ini merancang mesin pengeruk isi buah markisa berbasis mikrokontroler dan elektro pneumatik. Mesin ini menggunakan mikrokontroler sebagai pusat pengendali. Pertama markisa melewati sensor infrared yang berfungsi untuk mendeteksi buah dan menghitung jumlah buah masuk, setelah itu Mikrokontroler membaca dan mengaktifkan relay kontrol dari motor DC, selang 0,5 detik silinder 1 aktif mendorong ke bawah Motor DC sehingga markisa terserut selama 3 detik, lalu pneumatik mendorong ke atas ke posisi normal, selang 0,5 detik silinder 2 aktif mendorong kulit markisa yang telah diserut keluar (terbuang), berproses selama 1 detik dan kembali posisi normal, jika ada markisa masuk lagi alat berfungsi kembali dan begitu seterusnya. Hasil penelitian menghasilkan mesin pengeruk buah markisa yang berdimensi panjang 300 mm x lebar 300 mm x tinggi 600 mm dan daya motor 240 watt atau 0.321 HP, serta kapasitas produksi 54 kg/jam

Statistical and Machine Learning approach in forex prediction based on empirical data

This study proposed a new insight in comparing common methods used in predicting based on data series i.e statistical method and machine learning. The corresponding techniques are use in predicting Forex (Foreign Exchange) rates. The Statistical method used in this paper is Adaptive Spline Threshold Autoregression (ASTAR), while for machine learning, Support Vector Machine (SVM) and hybrid form of Genetic Algorithm-Neural Network (GA-NN) are chosen. The comparison among the three methods accurate rate is measured in root mean squared error (RMSE). It is found that ASTAR and GA-NN method has advantages depend on the period time intervals

Development of a Color-Based Image Recognition System for Robotic Sorting and Picking

This research presents the development of an automated object sorting and picking system using a robotic arm controlled by colour-based image recognition. The system is designed to enhance efficiency and accuracy in manufacturing processes by eliminating the need for manual sorting. A Dobot Magician robotic arm, an Arduino microcontroller, a conveyor belt, a photoelectric sensor, and a camera are integrated to achieve this goal. Colour segmentation is implemented using the HSV colour space, enabling the system to accurately classify objects based on colour. Experimental results demonstrate the system's ability to successfully sort objects of three colours in a random sequence with 100% accuracy over ten trials.Penelitian ini menyajikan pengembangan sistem otomatis untuk menyortir dan mengambil objek menggunakan lengan robotik yang dikendalikan oleh pengenalan gambar berbasis warna. Sistem ini dirancang untuk meningkatkan efisiensi dan akurasi dalam proses manufaktur dengan menghilangkan kebutuhan akan penyortiran manual. Sebuah lengan robotik Dobot Magician, mikrokontroler Arduino, sabuk konveyor, sensor fotoelektrik, dan kamera diintegrasikan untuk mencapai tujuan ini. Segmentasi warna diterapkan menggunakan ruang warna HSV, memungkinkan sistem untuk mengklasifikasikan objek berdasarkan warna dengan akurat. Hasil eksperimen menunjukkan kemampuan sistem untuk berhasil menyortir objek dengan tiga warna dalam urutan acak dengan akurasi 100% selama sepuluh uji coba

The Simulation of Vehicle Counting System for Traffic Surveillance Using Viola Jones Method

Traffic congestion in Makassar has occurred over the last 3 years due to the increase of\ud vehicle amount with the lack of adequate road space. To solve the problem, the Intelligent Transportation System (ITS) is needed. One of the research topics in ITS is determining the turnover time of traffic lights based on the number of vehicles in the road. Viola-Jones method can be used to count the number of vehicles by detecting\ud objects. Therefore, this study shows a simulation of the number of vehicles using the Viola-Jones\ud method as an initial step to implement the ITS in\ud Makassar. The data used is a front view car\ud pictures. Simulation results show that the average\ud accuracy rate of the detection is determined by the\ud number of samples. For example, the average of the\ud highest detection accuracy is 92% by using 150\ud positive samples and 300 negative samples were\ud applied to 30 test samples

Sosialisasi Rekondisi UMKM di Kelurahan Borongloe Kabupaten Gowa dengan Teknologi Informasi dan BMC di Era Pandemi Covid 19

Pandemi Covid 19 menyebabkan banyak UMKM yang terpaksa merayap dan bahkan harus ditutup karena tidak bisa beradaptasi dengan new normal dari konsumen. Untuk itu dalam pengabdian ini tim memberikan sosialisasi awal akan Teknik Business Model Canvas maupun pendekatan Teknologi Informasi khsusnya ke mitra UMKM yang ada di keluranhan Borongloe Kabuoaten Gowa. Proses sosialisasi berlangsung sangat baik dan dioandu langsung oleh Lurah Borongloe. Metode sosialisasi dengan gambaran BMC dan TI pada sei awal dan dilanjutkan dengan diskusi setiap UMKM yang hadir. Terlihat dari hasil diskusi banyaknya UMKM yang tidak menjalan usahanya dengan prosedur BMC maupun pendekatan TI yang benar