The alternative approach to predict the classification of HB vaccination status is using a machine learning approach such as random forest and naive Bayes classifier. However, for imbalanced classification, the algorithms are biased towards the majority class. To increase the accurate prediction of the classifier, we consider the Synthetic Minority Oversampling Technique (SMOTE) to have more balanced data. The purpose of this study was to compare the performance of SMOTE in the random forest and naive Bayes classifier for imbalanced Hepatitis-B vaccination data. The study used the National Socio-Economic Survey data in 2017 for Aceh province with 2264 cases and 14 variables. The results show that the application of SMOTE in the random forest and naive Bayes classifier improves the accuracy of identification of Hepatitis-B non-vaccination status by 30.08% and 26.09%, respectively, compared to non-SMOTE. Random forest with SMOTE is the best model for classification HB vaccination status. The most important factors that influence the Hepatitis-B vaccination status of Aceh province are the mother’s last education, mother’s occupation, father’s occupation, father’s previous education, and the number of health facilities.

Landfill in Bantargebang integrated solid waste treatment facility received 85% of the municipal solid waste of Jakarta Province in 2021. Since it is almost filled, the government operated a landfill mining (LM). This research aimed to characterize and evaluate the humate soil potential from mined material (MM) for agriculture. The weight, volume, and density of MM were measured on randomly selected collection vehicles in the LM location for eight consecutive days on March 2–15, 2022. Composition analysis was conducted by sieving and classifying samples into fine (FF), medium (MF), and coarse fractions (CF) of < 10 mm, 10–30 mm, and > 30 mm, respectively. MM had a weight, volume, and density of 81.43±13,03 tons/day, 147.84±33.31 m3/day, and 0.56±0.06 tons/m3, respectively. FF had soil-like material of 13.96±1.12%, while MF was 18.74±1.10%. Both fractions contained moisture, volatile solids, ash, carbon, nitrogen, and C/N ratio of 56.92–63.47%, 69.17–74.81%, 25.19–30.83%, 52.87– 55.67%, 1.89–2.10%, and 25.17–29.46, respectively. These characteristics met compost quality standards, except for high moisture content, which made it suitable for agriculture after moisture reduction. Meanwhile, plastics and wood-dominated CF were suitable for non-agriculture. A toxicological test is recommended to ensure the humate soil’s safe use.

<p><strong>ABSTRACT.</strong> The high food industry waste can cause pollution including a pungent odor and the emergence of bacteria that cause disease. Egg shells are confectionery waste that is commonly found in household waste and the food industry. The accumulation of eggshell waste can cause various diseases such as abdominal pain, diarrhea, fever, and cramps in the stomach caused by Salmonella bacteria. Thus, further processing of egg shells is required. In this study, eggshell waste was processed into heterogeneous catalysts of CaO. This heterogeneous CaO catalyst has the potential to be used as a catalyst in the manufacture of biodiesel. In this process, egg shells are processed by the calcination method. The variables used are shell type and calcination temperature. The shell variables used were chicken egg shells, duck egg shells, and quail egg shells. While the temperature variables used are at temperatures of 600 ºC and 800 ºC. To determine the quality of heterogeneous CaO catalyst, characteristic tests were varied out in the form of SEM-EDX and FTIR. From the analysis, the most effective results were in the form of chicken eggshell variables at a calcination temperature of 800 ºC.</p><p><strong>Keywords: </strong>Calcination, CaO, Catalyst, Shell, Waste</p>