Otomasi dan Monitoring Hidroponik pada Tanaman Selada dengan Metode Sonic Bloom Berbasis IoT

Tanaman selada khususnya selada krop (Lactuca Sativa L) merupakan sayuran dengan nilai ekonomi tinggi, nilai gizi besar, serta dengan bentuk yang menarik, sehingga membuat tanaman selada mempunyai potensi besar untuk dibudidayakan. Hal ini dibutuhkan pengembangan inovasi dalam produktivitasnya. Inovasi teknologi Sonic Bloom, yang memanfaatkan gelombang suara frekuensi tinggi, dimanfaatkan sebagai terobosan untuk membuat produktivitas tanaman selada tumbuh lebih baik. Hal ini disebabkan gelombang suara frekuensi tinggi, berfungsi untuk memacu membukanya mulut daun (stomata) yang dipadu dengan pemberian nutrisi. Sedangkan IoT dapat diimplementasikan pada bermacam bidang sehingga bermanfaat dan mempermudah kegiatan hidup manusia, antara lain pada sektor pertanian untuk memberikan kemudahan dalam menjalankan usaha. Kemudahan tersebut antara lain berupa pengumpulan data suhu, kelembaban, dan kecepatan angin, yang digunakan dalam mengambil keputusan untuk meningkatkan kualitas dan kuantitas dalam mengelola tanaman. Penerapan IoT mempermudah petani untuk mengecek suhu dan kelembaban tanah dari jarak jauh. Data ini nantinya akan digunakan untuk program pengairan dan pemupukan agar lebih presisi. Tujuan dari penelitian ini adalah untuk melakukan otomatisasi dan monitoring hidroponik pada tanaman selada dengan menggunakan metode Sonic Bloom berbasis IoT. Diharapkan melalui penelitian ini dapat mengetahui tingkat efektivitas dari perbandingan 3 musik yang berbeda dengan metode Sonic Bloom terhadap tumbuh tanaman selada. Evaluasi dengan membandingkan tanaman saat masa tanam dan pasca panen. Pada penelitian ini didapatkan hasil bahwa tanaman selada menggunakan lagu kicauan burung lebih efektif dalam meningkatkan laju pertumbuhan dibandingkan perlakuan musik gamelan dan instrumen pop dengan rata-rata pertumbuhan tinggi tanaman sebesar 0,54 cm, rata-rata pertumbuhan tinggi daun sebesar 0,51 cm, rata-rata lebar daun sebesar 0,19 cm, fresh weight sebesar 24,7 gram, dan dry weight sebesar 1,7 gram

Key Frame Generation to Generate Activity Strip Based on Similarity Calculation

Management of video data is done for several purposes, such as to make the information more meaningful. Research has been conducted to manage the video in terms of detecting activity in a video. There are three stages to generate activity strip: the data source stage (preparation of the frames), the processing stage (analysis of the activity), and the final stage (the collection of key frames). The generation of activity strip is done by calculating the difference of the pixel values of two frames to detect a similarity. In this research, we used SAD (Sum of Absolute Difference) method to calculate the value of the difference of the frame. Similar frames can be grouped in the same cluster. Each cluster is considered as one frame (or multiple frames) to serve as a key frame. The key frames are used for the representation of the activity strip. A collection of activity strip will be arranged sequentially and continuously for the activity generation

Keyframe Selection of Frame Similarity to Generate Scene Segmentation Based on Point Operation

Video segmentation has been done by grouping similar frames according to the threshold. Two-frame similarity calculations have been performed based on several operations on the frame: point operation, spatial operation, geometric operation and arithmatic operation. In this research, similarity calculations have been applied using point operation: frame difference, gamma correction and peak signal to noise ratio. Three-point operation has been performed in accordance with the intensity and pixel frame values. Frame differences have been operated based on the pixel value level. Gamma correction has analyzed pixel values and lighting values. The peak signal to noise ratio (PSNR) has been related to the difference value (noise) between the original frame and the next frame. If the distance difference between the two frames was smaller then the two frames were more similar. If two frames had a higher gamma correction factor, then the correction factor would have an increasingly similar effect on the two frames. If the value of PSNR was greater then the comparison of two frames would be more similar. The combination of the three point operation methods would be able to determine several similar frames incorporated in the same segmen

Klasifikasi Objek Dalam Visi Komputer Dengan Analisis Diskriminan

A robotic sensor system is always supported by a computer system called 'computer vision'. The important conceptof computer vision is object classfifi cation. In this study two algorithms for object classifi cation in this system will becompared. Firstly, A simple method that do not need complex computation and that considered as an informal method iscalled binary tree decision structure. This method is based on modest caracteristic decriptors of an object such as verticalline, horizontal line or ellipse line. Unfortunately this method has weakness in recognize an image that contaminated by anoise. Secondly, a more formal method with high variability descriptors. In this contect a multivariate statistical approachnamed discriminant analysis is proposed as an alternative for object classifi cation. This method is operated by computationof a function called Fisher discriminant function that can be used for separating an object. From the data simulation andanalysis for calssifi cation of two object i.e. screw and bolt and three objects i.e. alphabet T,O and S it can be shown thatdiscriminant analysis approach can classify an object better than binary decision algorithm. The superority of discriminantmethod is especially seen when this method is applied for classifi cation of a noisy image of object

KLASIFIKASI OBJEK DALAM VISI KOMPUTER DENGAN ANALISIS DISKRIMINAN

A robotic sensor system is always supported by a computer system called 'computer vision'. The important conceptof computer vision is object classfifi cation. In this study two algorithms for object classifi cation in this system will becompared. Firstly, A simple method that do not need complex computation and that considered as an informal method iscalled binary tree decision structure. This method is based on modest caracteristic decriptors of an object such as verticalline, horizontal line or ellipse line. Unfortunately this method has weakness in recognize an image that contaminated by anoise. Secondly, a more formal method with high variability descriptors. In this contect a multivariate statistical approachnamed discriminant analysis is proposed as an alternative for object classifi cation. This method is operated by computationof a function called Fisher discriminant function that can be used for separating an object. From the data simulation andanalysis for calssifi cation of two object i.e. screw and bolt and three objects i.e. alphabet T,O and S it can be shown thatdiscriminant analysis approach can classify an object better than binary decision algorithm. The superority of discriminantmethod is especially seen when this method is applied for classifi cation of a noisy image of object

Pengaruh Berat Potong dan Harga Pembelian Domba dan Kambing Betina terhadap Gross Margin Jagal di Rumah Potong Hewan Mentik, Kresen, Bantul (The Effects of Slaughter Weight and Purchase Price of Female Sheep and Goats on the Butcher’s Gross Margin at Ment

This study was aimed to determine the effect of slaughter weight and purchasing price of female sheep and goats on the butcher’s gross margins at the slaughterhouse of Mentik, Kresen, Bantul. The study was conducted to determinethe production activities of butchers. Sixty heads of local female sheep and goats respectively, were used as samples. The animals were divided into two groups, based on its body weight, namely 10 to 14.99 kg (BP1) and 15 to 20 kg(BP2) of body weight. The data consisted of purchasing price, slaughter weight, variable cost, dressing and non carcass percentages and the butcher’s gross margin. Factorial analysis was used to determine the ratio between spesies andgroups which live weight is best for the production of sheep and female goats. Multiple linear regression analysis was used to determine the effect of slaughter weight and purchase price to production of female sheep and goats, andslaughter weight and variable costs to gross margin of sheep and female goats butchers. The results showed that Bligon female goats of 15-20 kg body weight has the highest value on production and gross margins, it was 9.83 kg and Rp.104,901.50, respectively. The purchasing price and the slaughter weight significantly and positively affecting the production of female local sheep and goats with R2 = 0.718, female goats has better production than sheep. Slaughterweight significantly and positively affecting the gross margin of the butcher. Variable costs significantly and negatively affecting the gross margins of the butcher with R2=0.665. Higher variable cost will reduce the butcher’s gross margin. There were differences in the gross margin of female sheep and goat. The Gross margin of female goats was better than the gross margins of sheep. It can be concluded that local female goat’s production and gross margin was better than sheep.(Key words: Female local sheep and goats, Dressing and non carcass percentages, Production and Gross margin

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PERINGKASAN VIDEO MENGGUNAKAN DETEKSI SCENE BERBASIS PERBEDAAN HISTOGRAM DARI FRAME KUNCI

Perkembangan teknologi video memunculkan berbagai gagasan dalam mengelola konten video diantaranya adalah melakukan peringkasan terhadap video tersebut. Peringkasan video dilakukan agar pemirsa tidak harus membaca seluruh konten video yang memerlukan waktu panjang dengan tetap memperoleh informasi sesuai dengan video aslinya. Hal ini bisa dilakukan dengan cara membagi video dalam bentuk frame dan membandingkan antar frame untuk menentukan segmen dari setiap adegan. Dari setiap segmen adegan itulah dipilih frame kunci yang digunakan untuk merepresentasikan tiap adegan dalam video. Dalam penelitian ini dilakukan analisis terhadap perbandingan antar frame untuk menentukan transisi antar frame. Transisi itulah yang digunakan dalam penetapan segmen adegan. Analisis perbandingan antar frame dilakukan dengan menggunakan metode perbedaan histogram, sum of absolut difference (SAD) dan operasi titik (perbedaan frame, koreksi gamma dan psnr). Penelitian ini juga menganalisis pemilihan frame kunci untuk mewakili beberapa frame dari video, serta menganalisis pembentukan segmen adegan dari suatu video. Untuk melakukan analisis dan evaluasi terhadap kinerja pembentukan segmen adegan ini maka dilakukan perhitungan nilai recall, precission dan f-measure terhadap semua video eksperimen. Setelah dilakukan analisis dan evaluasi maka diperoleh rata-rata nilai recall, precission dan f-measure berturut-turut sebagai berikut: 94.184%; 95.191%; 94.654%. Hasil perhitungan tersebut menunjukkan bahwa metode yang dipakai sebagai pembentukan segmen adegan ini mempunyai tingkat ketepatan sistem (precision) untuk menentukan frame kunci sesuai dengan yang diminta oleh pengguna adalah sebesar 95.191%. Perhitungan tersebut mempunyai tingkat keberhasilan sistem dalam menentukan frame kunci (recall) sebesar 94.184%. Sedangkan bobot harmonik untuk menentukan kesetaraan nilai evaluasi dan ukuran timbal balik antara nilai recall dan nilai precission (nilai F-Measure) adalah sebesar 94.654%