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Classification Using Naive Bayes and Decision Tree on Food Addiction

In food consumption, food addiction is behavioral and biological overlaps have been observed between eating and addictive disorders. Food addiction influence to healthy life. Food addiction caused by overeating, bingo eating, eating disorder, eating addiction, mindless eating, craving, chocaholic, and emotional eating. Determination between addiction and normal condition in food consumption, need to classification. Classification is very important in determine signs of food addiction. Classification using Naïve Bayes Algorithm and Decision Tree Algorithm. Class target is Class Normal and Class Addiction. Classification using Naïve Bayes Algorithm by criterion is Calorie Dense Food, Fatty Food, Sweet Food, Diet and Stress. This criterion as causal factor of food addiction. Classification using Decision Tree Algorithm by criterion is Stress, Fatty Food and Calorie Dense Food. This criterion as causal factor of food addiction. The experimental result is a Classification Model. This model became data source for national policy in public health.

Fuzzy Logic using Tsukamoto Model and Sugeno Model on Prediction Cost

—This paper aims to implement Fuzzy Logic for cost prediction. Fuzzy Logic using Tsukamoto Model and Sugeno Model. Predicted costs consist of communication cost, transportation cost, and social cost as the external cost. The external cost is one component of living cost. High external cost becomes one of the causes of the high cost of living. The high cost of living is one of the factors causing high-cost economy. In this case, the cost prediction using Fuzzy Logic. Experimental results show that Fuzzy Logic using Tsukamoto Model with value is 1891. Fuzzy Logic using Sugeno Model with value 1621. Both models produce a feasible cost prediction. Feasible is meaning accurate and proper (value cost between low cost and high cost from all of cost). There are 46.56 % of the population of middle class in Indonesia. This means that 46.56% of the population of Indonesia has the potential to reduce the high cost economy. High cost economy (living cost) can be reduced, it can drive economic growth (social cost) and be able to improve social welfare (social cost).

Fuzzy Logic using Tahani Model on Food Commodity

—Seasonal vegetable plants are one of food commodity in Indonesia which contributed significantly in supporting the growth of national economy. Fuzzy Logic using Tahani Model is appropriate to analyze the harvested area, production, and yields of food commodity. It is based on query in fuzzy database. It is also can be proposed as recommendation for both government and entrepreneur in achieving in the target based on query. Target is main priority of food commodity. Fuzzy query is harvested area (medium), production (sufficient), and yield (normal). Non fuzzy query is commodity (export commodities), harvest (plants harvested several times), and form of product (fresh fruit). Result showed that eggplant (0.6587), tomato (0.6023), cucumber (0.5865), capsicum frutescens (0.2901), and capsicum annum (0.1581) as recommendation for priority of national food policy.

Clustering using K-Means and Fuzzy C-Means on Food Productivity

This paper provided an overview of analysis and implementation clustering for food productivity. Food productivity is determined by food production. Rice is one of staple food in Indonesia. Rice production is influencing adequacy level of national food production. Rice productivity is very important to accomplishment food affordability. Rice productivity per province in Indonesia must be increased, because large population and high consumption. Rice productivity that fluctuates and tends to decrease, need to clustering to determinant category cluster of productivity. Clustering is using K-Means and Fuzzy C-Means. Method improvement of K-Means is modification Intra Cluster Distance and Inter Cluster Distance. Calculate distance (Inter Cluster Distance and Intra Cluster Distance) to evaluate the clustering results and to compare the efficiency of the clustering algorithms. Method improvement of Fuzzy C-Means is modification algorithm, alternative process and iteration. Data processing is using Excel software. Clustering produce three cluster (C1, C2, C3) is convergence. Measurement cluster based on comparison of membership cluster, consistency, and productivity. Membership cluster, there is point data anomaly (x22, x23, x29, x33). Consistency data on K-Means (C1 = 72.73%, C2 = 93.75%, C3 = 100%). Consistency data on Fuzzy C-Means (C1 = 100%, C2 = 88.33%, C3 = 87.50%). Rice Productivity is Cluster 1 (decrease), Cluster 2 (decrease, except 3 provinces), and Cluster 3 (increase, except 1 province). Majority in rice productivity is 70.59%. Result of clustering showed that majority rice productivity on category cluster is low productivity.

Multi-Attribute Decision Making Using Simple Additive Weighting and Weighted Product in Food Choice

—This paper provide an overview of the analysis and implementation Multi-Attribute Decision Making (MADM) for food selection or food choice. Food choice aim to find the solution on lack of food. Food choice can be doing with diversification. Food diversification aims to find best choice of food alternatives. Food alternative is rice, corn, cassava, potato, sago, sorghum, wheat, and analog rice. The method used is Simple Additive Weighting (SAW) and Weighted Product (WP). The use of this method is expected to help and provide the best decision in food choice. Alternative on MADM as data training, alternative on SAW method and alternative on WP method as data testing. Experimental result on SAW method, best alternative (highest value) is wheat with value 0.8833. On WP method, best alternative (highest value) is wheat with value 0.1563. On SAW method and WP method, decision is the same with wheat as best alternative in MADM on food choice

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Teknologi Komputasi Pangan (Teknologi Informasi, Sains Komputasi, dan Pangan Fungsional)

Fuzzy Logic using Tahani Model on Food Commodity

Seasonal vegetable plants are one of food commodity in Indonesia which contributed significantly in supporting the growth of national economy. Fuzzy Logic using Tahani Model is appropriate to analyze the harvested area, production, and yields of food commodity. It is based on query in fuzzy database. It is also can be proposed as recommendation for both government and entrepreneur in achieving in the target based on query. Target is main priority of food commodity. Fuzzy query is harvested area (medium), production (sufficient), and yield (normal). Non fuzzy query is commodity (export commodities), harvest (plants harvested several times), and form of product (fresh fruit). Result showed that eggplant (0.6587), tomato (0.6023), cucumber (0.5865), capsicum frutescens (0.2901), and capsicum annum (0.1581) as recommendation for priority of national food policy.

INFERENCE MENGGUNAKAN FORWARD CHAINING PADA FOOD AFFORDABILITY

<p>Price, consumption, and production influence global food estimation. Food consumption and food production needs improving affordability, government policy and expert proposition in decision making. Expert have developed a scenario  combining direct weather impact on key grain producing regions, indirect impact through crop pathogens, consequences for markets, and stock of global food. Global food production tended to increase, meanwhile consumption under production. In Indonesia, production is under consumption. It is anomaly by global situation and world condition. Government policy is tariff, export, and import. Export food influenced by price food commodity is not stable, meanwhile food import tended to increase because by national food consumption increasing demand. Government policy and expert proposition to modern society, need Expert System is increasing rapidly. In Artificial Intelligence, Expert System is a computer system that emulates the decision making ability of a human expert. There are two reasoning strategies in Expert System: Forward Chaining and Backward Chaining. The aim of this paper is to identify which reasoning strategy system (Forward Chaining). This paper focus on the concept of knowledge representation in Artificial Intelligence and implementing of Forward Chaining on Food Affordability. Food Affordability used to planning of food in the future. Finally, Inference using Forward Chaining on Food Affordability, the result it is good and can be used to planning and policy on national food.</p>

Fuzzy Logic using Tsukamoto Model and Sugeno Model on Prediction Cost

This paper aims to implement Fuzzy Logic for cost prediction. Fuzzy Logic using Tsukamoto Model and Sugeno Model. Predicted costs consist of communication cost, transportation cost, and social cost as the external cost. The external cost is one component of living cost. High external cost becomes one of the causes of the high cost of living. The high cost of living is one of the factors causing high-cost economy. In this case, the cost prediction using Fuzzy Logic. Experimental results show that Fuzzy Logic using Tsukamoto Model with value is 1891. Fuzzy Logic using Sugeno Model with value 1621. Both models produce a feasible cost prediction. Feasible is meaning accurate and proper (value cost between low cost and high cost from all of cost). There are 46.56 % of the population of middle class in Indonesia. This means that 46.56% of the population of Indonesia has the potential to reduce the high cost economy. High cost economy (living cost) can be reduced, it can drive economic growth (social cost) and be able to improve social welfare (social cost).

PREDIKSI CLUSTERING, CALCULATION DAN CLASSIFICATION FRUIT AND VEGETABLE CONSUMPTION

<p class="Abstract">Prediction model using combination of K-Means Clustering, Excel Function, and Naïve Bayes Classifier. Process is dataset, clustering, calculation, classification and prediction. Dataset source on BPS 2013 about consumption of fruit and vegetable. Clustering using K-Means Clustering. Clustering by output Cluster 1, Cluster 2, and Cluster 3. Calculation using Excel. Calculation by output Priority Yes and Priority No. Classification using Naïve Bayes Classifier. Classification by output Class Good and Class Bad. All data processing for clustering, calculation, and classification using Excel. Experimental results on BPS 2013 Dataset show percentage of fruit consumption 42,42% Class Good (class above average) and percentage fruit consumption of fruit consumption 57,58% Class Bad (class below average). Percentage of vegetable consumption 45,45% Clas Good (class above average) and percentage of vegetable consumption 54,55% Class Bad (class below average). Clustering, calculation and classification can be combined becamed prediction model.<em></em></p><em>Key words: clustering, calculation, classification, fruit and vegetable consumption</em>

TEKNIK MENYARING INFORMASI INTERNET UNTUK MENDAPATKAN MATERI BELAJAR

<p>A important component of teaching learning  process  is  teaching  material.    A  good  teaching  material  will  produce  optimal  and  new  (recent)  teaching  learning  process.  Internet is a source where we can find and get the new teaching recently. To find the teaching material, we have to know the computer identity where the teaching material is located. The Internet can be categories into 2 categories namely edu (education) for  computer which located in United State and ac (academic) which is followed by country  code for computer which located in the outside of United State. Filtering information with Search Engine include Power Searching Conceptual with application need technique for get information is expected. </p><p>Key words: filter, information, website, teaching material, find and get</p>

METODE AHP DALAM SISTEM PENUNJANG KEPUTUSAN PEMILIHAN DOSEN DENGAN MENGGUNAKAN PERANGKAT LUNAK EXPERT CHOICE

<p style="margin: 1em 0px; color: #111111; font-family: Arial, Verdana, Helvetica, sans-serif; font-size: 11.2px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #fafafa;">This research is conducted to apply the Analytical Hierarchy Process (AHP) method applied as model of Decision Support System (DSS) in choosing lecturer at STIKOM Pelita Indonesia Pekanbaru. Data collected by through observation and interview done in shares of administration academic at college. Here in after data analyzed to learn the pattern from method used and added with the reference from literature. Experiment done use the Expert Choice software known that method can yield the optimal decision in election instructor staff. There by the method recommended to be applied to getting optimal result.</p><p style="margin: 1em 0px; color: #111111; font-family: Arial, Verdana, Helvetica, sans-serif; font-size: 11.2px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #fafafa;"> </p><p style="margin: 1em 0px; color: #111111; font-family: Arial, Verdana, Helvetica, sans-serif; font-size: 11.2px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #fafafa;">Keywords: Decision Support System, Analytical Hierarchy Process, Expert Choice Software</p>