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<p><strong>ABSTRAK</strong></p><p><em>Komoditas tembakau Madura merupakan salah satu komoditas perkebunan yang yang banyak dibudidayakan oleh petani Madura. Akan tetapi pada kenyataannya, petani tembakau madura masih belum bisa menjual tembakaunya dengan harga yang tinggi. Ini dikarenakan panjangnya rantai pasok penjualan tembakau Madura. Terdapat beberapa interrmediate trader dalam rantai pasok tersebut dan setiap intermediate trader juga mengambil keuntungan penjualan. Penelitian ini bertujuan untuk memperbaiki mekanisme rantai pasok penjualan tembakau Madura. Metode yang digunakan pada penelitian ini yaitu analisa SWOT dan analisa rantai pasok. Data diperoleh dengan melakukan FGD dan wawancara. Hasil penelitian berupa rekayasa rantai pasok penjualan komoditas tembakau dengan menciptakan marketplace. Diharapkan penelitian ini bisa menjadi input bagi penelitian selanjutnya dalam proses desain dan membangun marketplace.</em></p><p><em>Kata Kunci: </em><em>tembakau madura, SWOT, rantai pasok, marketplace </em><em></em></p><p align="center">SUPPLY CHAIN ANALYSIS OF MADURA TOBACCO COMMODITIES</p><p><strong> </strong></p><p><strong>ABSTRACT</strong></p><p><em>Product of Madura tobacco is one of the plantation commodities which is widely cultivated by Madurese farmers. In fact, though Madura tobacco farmers still could not sell their tobacco at a higher price. This is due to the supply chain of Madura tobacco sales which has a long network. There are several intermediate traders in the supply chain network and each intermediate trader takes sales profits as well. This research aims to analyze and remodel the supply chain mechanism for Madura tobacco sales. This study employed a SWOT analysis and supply chain analysis. Data were obtained by conducting FGDs and interviews. The research results from a new model of the sales supply chain for Madura tobacco by creating a marketplace. It is expected that this work can be useful as an input for further study in designing and building a marketplace</em>.</p><em>Keywords: Madura tobacco, SWOT, supply chain, marketplace</em>

Mobile robot merupakan salah satu kategori robot yang memiliki fungsi untuk berpindah tempat. Pengembangan dari mobile robot yaitu implementasi perubahan lintasan mendatar¸ tanjakan serta turunan. Ketika robot berjalan terdapat gangguan tak terukur dari luar yang mempengaruhi respon sistem. Hal ini menyebabkan kecepatan robot berubah-ubah, sehingga dibutuhkan suatu mekanisme yang mampu membuat mobile robot menjaga stabilitas dan kemampuan jelajah untuk kestabilan kecepatan mobile robot. Penelitian ini menerapkan metode PID untuk mengatur servo berdasarkan input dari gyro Z, sehingga membuat mobile robot berjalan tanpa keluar dari lintasannya dengan nilai konstanta Kp = 5, Ki = 0 dan Kd = 1. Respon sistem mencapai stedy state dengan nilai servo 90°, dari detik ke-8 sampai detik ke-13,5. Mobile robot dapat menjaga kestabilan kecepatan ketika melewati lintasan mendatar, tanjakan serta turunan dengan menerapkan metode fuzzy logic controller dan kontrol PID dengan nilai konstanta Kp = 1,5, Ki = 0 dan Kd = 0,2. Respon yang diberikan oleh sistem mencapai steady state nilai kecepatan 0,88 pada detik ke-16,4. Sensor yang digunakan untuk mengetahui kemiringan lintasan yaitu sensor MPU6050 nilai gyro Y dan sensor rotary encoderMobile Robot Speed Stability on Flat Road, Upward and Descend TrajectoriesAbstractMobile robot is a robot that can move to other places. The development of a mobile robot is the implementation of changes in in flat, upward and descending trajectories. When robot runs there are immeasurable interference that affects the system response. This causes robot speed is change, so we need a solution for mobile robot to maintain stability and movement capability for stability the robot speed. This research applies the PID method to set the servo based on the input from the gyro Z, so as to make the mobile robot run without leaving its path with constant values Kp = 5, Ki = 0 and Kd = 1. This research applies PID method to set servo motor based on input from gyro Z, so as to make the mobile robot run on path with constant values Kp = 5, Ki = 0 and Kd = 1. The system response reaches steady state when the servo value is 90 °, from the 8th second to the 13.5th second. Mobile robots can maintain the stability of speed when flat road, upward, and descend trajectories by applying the fuzzy logic controller and PID control methods with constant values of Kp = 1.5, Ki = 0 and Kd = 0.2. The response given by the system reaches a steady state speed value of 0.88 at 16.4 seconds. The sensors used to determine the slope of the track are the MPU6050 Y gyro sensor and rotary encoder sensor.Keyword: mobile robot, MPU6050, rotary encoder sensor, fuzzy logic controller and PID control.

Unmanned Aerial Vehicle (UAV) has many uses, including aerial photography, aerial mapping and monitoring activities. Quadcopter is a type of UAV that uses four rotors. The speed of each rotor has a considerable influence on the movement. The quadcopter movement can be done in this research, namely the process of arming, taking off, hover, and landing on decisions made by the system (autonomous). How the quadcopter achieves a balance in its movement to be stable and responsive requires a method. One method that is suitable for processing stability is the method (PID). The PID method has three main parameters, namely Proportional (Kp), Integral (Ki), Derivatives (Kd), with the determination of the constant through the trial and error process to obtain the optimal stability value as the purpose of this study. But the load of the quadcopter and the wind tightness around it is very influential to get the quadcopter movement to survive in stable conditions. Through a series of experimental processes carried out to produce the best constant values, at Kp = 1.3, Ki = 0.04, and Kd = 18, where the quadcopter is able to survive for 20 sec at the same relative point from the time of departure. The addition of GPS sensors in advanced research will be able to make this quadcopter move stable with the monitored position.

This paper addresses the problem of obstacle avoidance in mobile robot navigation systems. The navigation system is considered very important because the robot must be able to be controlled from its initial position to its destination without experiencing a collision. The robot must be able to avoid obstacles and arrive at its destination. Several previous studies have focused more on predetermined stationary obstacles. This has resulted in research results being difficult to apply in real environmental conditions, whereas in real conditions, obstacles can be stationary or moving caused by changes in the walking environment. The objective of this study is to address the robot’s navigation behaviors to avoid obstacles. In dealing with complex problems as previously described, a control system is designed using Neuro-Fuzzy so that the robot can avoid obstacles when the robot moves toward the destination. This paper uses ANFIS for obstacle avoidance control. The learning model used is offline learning. Mapping the input and output data is used in the initial step. Then the data is trained to produce a very small error. To support the movement of the robot so that it is more flexible and smoother in avoiding obstacles and can identify objects in real-time, a three wheels omnidirectional robot is used equipped with a stereo vision sensor. The contribution is to advance state of the art in obstacle avoidance for robot navigation systems by exploiting ANFIS with target-and-obstacles detection based on stereo vision sensors. This study tested the proposed control method by using 15 experiments with different obstacle setup positions. These scenarios were chosen to test the ability to avoid moving obstacles that may come from the front, the right, or the left of the robot. The robot moved to the left or right of the obstacles depending on the given Vy speed. After several tests with different obstacle positions, the robot managed to avoid the obstacle when the obstacle distance ranged from 173 – 150 cm with an average speed of Vy 274 mm/s. In the process of avoiding obstacles, the robot still calculates the direction in which the robot is facing the target until the target angle is 0.