Abdul Halim

The Aceh Jinayat Qanun, which is often considered violating Human Rights, has become the choice of the non-Muslim minorities as their rational choice. This study aims to analyze non-Muslims’ choice of The Aceh Jinayat Qanun implemented by the Sharia Court in Aceh and its underlying motives. This study relies on field research involving observations, in-depth interviews with Sharia Court judges, Head of the Islamic Sharia Service, Acehnese clerical figures, and Non-Muslims involved in criminal cases handled by the Sharia Courts. This study also analyzes Sharia Court decisions on criminal cases involving non-Muslims and various related documents issued by the Sharia Courts, police, and prosecutors. The study was undertaken between July 2017 and March 2020. The study shows that the Acehnese non-Muslims do not select The Aceh Jinayat Qanun because of its religious values but based on practicality, efficiency, and socio-cultural consideration. The Sharia-based sentences, which have often been conceived as inhuman and violate fundamental human rights, are chosen and become the rational choice for non-Muslims in solving their legal issues. This paper concludes that the implementation of the Sharia on non-Muslim has not always been negative. This paper demonstrates non-Muslims’ interest to choose Sharia-based criminal justice or The Aceh Jinayat Qanun over the Criminal Code. This can be seen as their rational choice over a more efficient, low-cost, effective, and fast legal process offered by The Aceh Jinayat Qanun sentencing system.

Nature has the ability of sustainability and improvisation for better survival. This unique characteristic reflects a pattern of optimization that inspires the computational intelligence toward different scopes of optimization: a nondeterministic optimization approach or a nature-inspired metaheuristic algorithm. To date, there are many metaheuristic algorithms introduced with good promising results and also becoming a powerful method for solving numerous optimization problems. In this paper, a new metaheuristic algorithm inspired from a plant growth system is proposed, which is defined as tree physiology optimization. A plant growth consists of two main counterparts: plant shoots and roots. Shoots extend to find better sunlight for the photosynthesis process that converts light and water supplied from the roots into energy for plant growth; at the same time, roots elongate in the opposite way in search for water and nutrients for shoot survival. The collaboration from both systems ensures plant sustainability. This idea is transformed into an optimization algorithm: shoots with defined branches find the potential solution with the help of roots variable. The shoots-branches extension enhances the search diversity and the root system amplifying the search via evaluated fitness. To demonstrate its effectiveness, two different classes of problem are evaluated: a continuous benchmark test function compared to particle swarm optimization and genetic algorithm and an NP-hard problem with the traveling salesman problem compared to GA and nearest-neighbor algorithm. The simulation results show that TPO outperforms PSO and GA in all problem characteristics. In the TSP, TPO has a comparable result to GA.